{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "520f9e2f-d1ec-4d53-bc24-5fd86c8ac860",
   "metadata": {},
   "source": [
    "# 12.2 案例实战 - 人脸识别模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd35704d-d297-4a14-af87-f14af585f128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "案例实战 - 人脸识别模型\n",
      "============================================================\n",
      "\n",
      "=== 步骤1：数据读取和预处理 ===\n",
      "找到 400 张人脸图片\n",
      "特征矩阵形状: (400, 1024)\n",
      "标签数量: 400\n",
      "人员数量: 40\n",
      "\n",
      "=== 显示样本人脸图片 ===\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 标签分布分析 ===\n",
      "各人员样本数量:\n",
      "1     10\n",
      "2     10\n",
      "3     10\n",
      "4     10\n",
      "5     10\n",
      "6     10\n",
      "7     10\n",
      "8     10\n",
      "9     10\n",
      "10    10\n",
      "11    10\n",
      "12    10\n",
      "13    10\n",
      "14    10\n",
      "15    10\n",
      "16    10\n",
      "17    10\n",
      "18    10\n",
      "19    10\n",
      "20    10\n",
      "21    10\n",
      "22    10\n",
      "23    10\n",
      "24    10\n",
      "25    10\n",
      "26    10\n",
      "27    10\n",
      "28    10\n",
      "29    10\n",
      "30    10\n",
      "31    10\n",
      "32    10\n",
      "33    10\n",
      "34    10\n",
      "35    10\n",
      "36    10\n",
      "37    10\n",
      "38    10\n",
      "39    10\n",
      "40    10\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import xgboost as xgb\n",
    "from lightgbm import LGBMClassifier\n",
    "from PIL import Image\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"案例实战 - 人脸识别模型\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 1. 读取和预处理数据\n",
    "print(\"\\n=== 步骤1：数据读取和预处理 ===\")\n",
    "\n",
    "import os\n",
    "directory = 'olivettifaces'\n",
    "names = os.listdir(directory)\n",
    "\n",
    "print(f\"找到 {len(names)} 张人脸图片\")\n",
    "\n",
    "# 提取特征变量\n",
    "X = []  # 特征变量\n",
    "y = []  # 目标变量\n",
    "\n",
    "for i in names:\n",
    "    # 读取图片\n",
    "    img = Image.open(os.path.join(directory, i))\n",
    "    img = img.convert('L')  # 转为灰度图\n",
    "    img = img.resize((32, 32))  # 调整大小\n",
    "    arr = np.array(img)\n",
    "    \n",
    "    # 展平为一维特征\n",
    "    X.append(arr.flatten().tolist())\n",
    "    \n",
    "    # 提取标签（人员ID）\n",
    "    person_id = int(i.split('_')[0])\n",
    "    y.append(person_id)\n",
    "\n",
    "# 转换为DataFrame\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.Series(y)\n",
    "\n",
    "print(f\"特征矩阵形状: {X.shape}\")\n",
    "print(f\"标签数量: {len(y)}\")\n",
    "print(f\"人员数量: {len(y.unique())}\")\n",
    "\n",
    "# 显示一些样本图片\n",
    "print(\"\\n=== 显示样本人脸图片 ===\")\n",
    "fig, axes = plt.subplots(2, 5, figsize=(15, 6))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i in range(10):\n",
    "    img_data = X.iloc[i].values.reshape(32, 32)\n",
    "    axes[i].imshow(img_data, cmap='gray')\n",
    "    axes[i].set_title(f'Person {y.iloc[i]}')\n",
    "    axes[i].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 标签分布分析\n",
    "print(\"\\n=== 标签分布分析 ===\")\n",
    "label_counts = y.value_counts().sort_index()\n",
    "print(\"各人员样本数量:\")\n",
    "print(label_counts)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "label_counts.plot(kind='bar')\n",
    "plt.title('各人员样本数量分布')\n",
    "plt.xlabel('人员ID')\n",
    "plt.ylabel('样本数量')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pie(label_counts.values, labels=[f'Person_{i}' for i in label_counts.index], \n",
    "        autopct='%1.1f%%')\n",
    "plt.title('人员占比分布')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1f86eb-6f34-472d-97a2-33e72a001466",
   "metadata": {},
   "source": [
    "## 特征工程和数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fe73bd7-4abe-4c26-a9f0-597f7d541805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤2：特征工程和数据预处理 ===\n",
      "特征标准化...\n",
      "应用PCA降维...\n",
      "原始特征维度: 1024\n",
      "PCA后特征维度: 50\n",
      "PCA解释方差比: 0.8796\n"
     ]
    },
    {
     "data": {
      "image/png": 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0Sd177719njPY6z6YNzrHkR73oHvvvVcBKpFIDLrPH//4RzVr1izl8/nU/Pnz1a9+9Stlmmafa3vqqafUmWeeqfx+v5o5c6a66667+hyjvb1dvf/971exWEyVl5erd73rXaqpqemw23nBBRcoQBmGoSZMmKBuvvlm1d7e3mefo/2bD+W9K4QQY5GmlBTPFkIIIYQQQhw9SegWQgghhBBCDAsJLoQQQgghhBDDYkwGFwcT5hKJxGg3RQghhBBCCHGYxlxw8eCDDzJ58mRuvPFGJkyYwIMPPgjA+vXrWbhwIfF4nFtuuWXY66sLIYQQQghxPHnLW97Su87S8uXLmTNnDlVVVXzrW98atTaNqeAimUzy0Y9+lKeeeop169Zx1113ccstt1AsFrnqqqtYsGABK1asYOPGjf0WrBJCCCGEEOLN4he/+AV/+9vfAGhvb+fqq6/muuuu4/nnn+cXv/gFTz755Ki0a0xVi2psbOSpp57ife97HwBr167l3HPP5ec//zkf/vCHaWpqIhQKsWbNGm6++WaeeeaZUW6xEEIIIYQQI6urq4uTTjqJ8vJyPvvZz9Ld3c0Pf/hDNm7ciKZp/PGPf+TBBx/k/vvvH/G2jang4rVs2+amm27CdV2mTZvGiy++yMMPPwz0rFhaWVlJV1fXgM8tFosUi8Xe3z3Po6uri8rKSjRNG5H2CyGEEEKIw6eUIp1OM27cOHR9TE2uAaBQKFAqlY7JsZVS/fqofr8fv98/4P433HADgUCAfD7PkiVLWL58OcFgkP/5n/8BYP/+/Vx00UVs2rTpmLT3UMbkCt1r1qzhoosuwufzsWnTJm677TamTJnSu13TNAzDIJFIEI/H+z3/jjvu4Etf+tJINlkIIYQQQgyDxsZGJkyYMNrN6KNQKBCf2ECho/2YHD8SiZDJZPo8duutt/LFL36x375PPvkkf//739mwYQMf+9jHAEilUpx00km9+0SjUfbt23dM2vpGxmRwMW/ePB599FE++clPcuONNzJt2rR+kVsgECCXyw0YXHzuc5/jU5/6VO/vyWSShoYGGhsbiUajx7z9QgghhBBiaFKpFBMnTqSsrGy0m9JPqVSi0NHOtY8+gxWODOux7WyG3192Xr9+6kCjFoVCgX/6p3/iBz/4QZ/XyTTNPvsf7CePhjEZXGiaxoIFC/jpT3/KtGnTuOOOO1i/fn2ffdLpND6fb8DnDzaMFI1GJbgQQgghhBjDxvIUdiscwRc5NsHP4fRTb7vtNhYuXMgVV1zR5/GKigra218dVTlUP/lYG1PBxfLly/nzn//MN7/5TQB8Ph+apjFnzhzuueee3v127dpFsVikoqJitJoqhBBCCCHEiPrlL39Je3s75eXlAORyOR544AEAzjnnnN79Vq1axfjx40ejiWOrFO3MmTO5++67ufvuu2lsbOTzn/88l112GZdffjmpVIp7770XgNtvv51LLrkEwzBGucVCCCGEEEKMjKeffpr169ezevVqVq9ezdVXX82Xv/xl9u7dy7PPPsvjjz+Obdt84xvfYOnSpaPSxjE1clFfX89DDz3EJz7xCT7zmc+wdOlSfvazn2GaJj/60Y+47rrruOWWW9B1nWXLlo12c4UQQgghhBgxr090j0QiVFVVUVVVxZ133snll19OJBKhvLx81NaEG7OlaAfS0tLCypUrWbRoEZWVlYf9vFQqRSwWI5lMSs6FEEIIIcQYNJb7awfb9q5nVw97zkUpk+aBc+cPy3Xv2rWLzZs3c/755xOJDG/i+eEaUyMXb6Surq5fAosQQgghhBACpkyZ0mf5htEwpnIuhBBCCCGEEMcvCS6EEEIIIYQQw0KCCyGEEEIIIcSwkOBCCCGEEEIIMSwkuBBCCCGEEEIMCwkuhBBCCCGEEMNCggshhBBCCCHEsJDgQojjRNHzaC/ZZF13tJsihBBCCDGg42oRPSHejDpLDr9r7eLRzhRpx8WnaZwfL+Mf6uJMCwVGu3lCCCGEEL1k5EKIMayj5PC5rU3c19xJt+0S0HVsBb9v6+bftzSxIZMf7SYKIYQQQvSS4EKIMewX+zpYn8kzMeCjzm9RZhpU+UymBH20lmy+s7sVV6nRbqYQQgghBCDBhRBjVtJ2+HtXmqipY+lan226plHrs9ieK7A2LaMXQgghhBgbJLgQYoxqLTlkHZeIYQy4PWTo2Eqxr1ga4ZYJIYQQQgxMggshxii/rmFoGs4g054OTofy6/LPWAghhBBjg/RKhBijGgI+ZoUDdNoOaoAAo9N2iFsmC6KhUWidEEIIIUR/ElwIMUZpmsa76ysI6jrNRbt3BMNTio6SQ971uLamnLglFaWFEEIIMTZIcCHEGHZevIxPTa4jbhk0FUrszhfZky+hafC++ko+MK5qtJsohBBCCNFLbnkKMca9pTrGOfEIzyUytNs2EcNgUXmYer9vtJsmhBBCCNGHBBdCHAeipsFbqmOj3QwhhBBCiEOSaVFCiGGhlBow8VwIIYQQbx4yciGEOCp780Uebk/yZFeKvKuYFPTx1uoYl1RG8UmZXCGEEOJNRYILIcQR25DJc+u2ZvYVS4QNA0vTWJPOsSad4+Vkls9NrZcAQwghhHgTkeBCCHFESp7HN3btp6VkMyXoR9c0ACoxyTguj3emOCkS5J11FaPcUiGEEEKMFLmlKIQ4Ii8ls+zOlaj3W72BxUER08DUNP7S3o3jSR6GEEII8WYhwYUQQ+B5Hh0dHbS0tJDL5Ua7OaNqT76EhyIwyLSnqGnQUrRJOM4It0wIIYQQo0WmRQkBJBIJtm7dyt69e/E8j/r6embNmkVtbS3QUwlp27ZtrF69mq6uLpRS+Hw+pk2bxsKFCwmHw6N8BSPP1DSU6nlttNeNXAC4SqFrGuYA24QQQghxYpLgQrzp7dmzhyeeeIJMJoNpmmiaRltbG5s3b+acc85h7ty5rFu3jmeffRbP8wiHw+i6TrFYZN26dbS3t3PFFVcQCoVG+1JG1PxoiKChk3Y9oqbRZ5tSiqTjcm55hPLXbRNCCCHEiUuCC/GmlslkePLJJ8lms1RUVPTegVdKkU6nee655wiHw7z88stomkY8Hu99rmmaBAIBWlpa2LBhAwsXLhytyxgVM0N+FpWH+XtnCp2ePAsATylaSjZBXefa2viAoxpCCCGEODFJzoV4U9uxYwfpdJry8vI+nWBN0ygrK6NYLPLSSy+Ry+WIRCL9nm8YBpZlsXnzZlzXHcmmjzpN0/jM5DoWx8tIOi47c0V25grsKZQI6Tofm1TLWeX9XzMhhBBCnLhk5EKcsJRS5PN5AILB4IB30FtbW9E0DX2ApGRN07Asi46OjkH3AbAsi0KhgG3bGMahpwB5nse+fftoamrCcRxisRhTp049bnM2YpbJ7TMnsDqd46XuLAXPY1zAx5J4GTV+a7SbJ4QQQogRJsGFOOF4nseWLVvYsGEDiUQCgOrqak4++WSmT58+5Gk6hmFg2/agicuO4+D3+zHNQ/9zyuVy/P3vf+8NLA5asWIF5557LjNnzhxSu8YKXdM4PRrm9OjxGSAJIYQQYvjItChxQlFK8dxzz/HEE0/Q2tqKruvouk5TUxOPP/44K1eu7LP/uHHjgJ6AZKBj2bbN9OnT8fv9A5ae9TyPYrHI9OnTDxlcKKV48skn2bVrF4FAgMrKSiorK4nH4+TzeZYtW0Zzc/NRXr0QQgghxOiS4EIcV9LpNDt27GDHjh2kUql+2xsbG1m3bh2WZVFWVkYgECAYDFJRUYFhGLzyyiu0tbX17j9t2jRisRjd3d19AgylFMlkkmAwyPz58zn55JMpFouk02k8z0MpRbFYJJFIEI/HmTt37iHb3dLSQmNjI2VlZfj9/t7HdV0nFotRLBbZsGHDMLxCQgghhBCjR6ZFieNCoVDg+eefZ/v27RQKBQACgQBTp07lnHPOIRgMAvDKK6+QTCaBngDBMAwikQhlZWWEw2G6urrYtm0bNTU1QE8uxqWXXspjjz1Gd3c30JNr4XkeoVCICy64gKqqKuLxOJZlsWHDBrq7u1FKYVkWEyZM4Pzzz6e8vPyQ7W9ubsa2bcrKyvpt0zSNQCDA3r17KZVK+Hy+YXrVRp5SivWZPGvTeTwUU4J+zoyF8Q2SryKEEEKIE4sEF2LMcxyHxx9/nF27dhEMBonHe8qb5vN5Nm7cSCaT4fLLL6e5uZktW7Zg2zaWZaHrOq7rkkgkyOfz1NTUYBgGXV1dfY5fV1fH29/+dnbs2EFzczOu61JXV8eMGTOIRqNAT97FmWeeySmnnMK+fft6k7Fra2sPK4fD8zw0TRt0X13X8TxvwOlZx4uOksPXd+1nVSpLwVMAGJrG1KCf/5hax+xwcJRbKIQQQohjTYILMebt2bOHPXv2UFZW1ueufigUwrIsGhsb2b59OytXrsTzPAzD6K3apOs6SikKhULvlKbXTkt67bFOOeUUTjnllEO2JRgMMm3atCFfw8GRDdd1B6woVSgUqK6uPm5HLUqex207mnk5maXWZ1Hn09E0jYLrsTVX4NZtzXxnziTqpIKUEEIIcUKTuQpizNuxYwdKqQE73pbV01ldvXo13d3dRKNRlFK9PwfzI3RdJ51Oo5Ri0qRJI30JTJ48mfLycpLJJEqpPtuKxSIAJ5100qDlbse6l5JZVqfzjPP7iJhG7whNwNCZFPDRVLD5a0dylFsphBBCiGPt+OzJiDeVbDZ7yE63YRhkMhkAotEolmVRLBbJ5/MUi0UKhQKlUolisUhlZSVTpkwZqab38vl8LFmypDfvI51Ok8vlSCQS5HI5ZsyYwezZs0e8XcPlpe4sjqcIGv3/Toam4dc1lnX1T8AXQgghxIlFpkWJMa+srIympqZBtzuOQzQa7U30Ppi/APSOEhz87/jx40dt6tGECRO4+uqr2bhxIzt27MB1Xerr65kzZw4zZ858wwX4xrKc53Go1BNL18i5x28+iRBCCAHwcfUtImp4+xEZVeKBYT3i6JLgQox5M2bMYOvWrRSLxX75EsViEV3XmTdvHi+88AJdXV199jsYZLiui9/vZ+vWrcyfP59YLDbi1wFQWVnJ+eefz3nnndebH3IiaAj48JTCUwp9gCgj53qcHg2NQsuEEEIIMZJkWpQY8yZOnMiMGTPIZDKkUikcx8FxHNLpNJlMhunTp3PSSScxe/Zsstlsb46FYRi9C9tZlkVlZSWFQoEdO3aM8hX1lJ89UQILgCUVZZRbJm0lp9+2lONiaBpLq0YnoBNCCCHEyJHgQox5uq5z4YUXctZZZxEMBslkMmQyGfx+P2eeeSYXXXQRhmGwaNEigsEgmqZh2za2beM4DqZpUlVV1bsWxsH8DDF8GoJ+bpxQDcDufJGE7ZC0Xfbmi3Q7LpdXxTg/3n+NDyGEEEKcWGRalDgumKbJmWeeyamnnkpXVxdKKSorK/tMkzoYRKRSKQzDQCmFaZoEg8HekrTAcVvudaz7h9o4dT6LP7Ql2JDJYyvFrHCQK2vKubwqhnEY64EIIYQQ4vgmwYU4rvj9furr6wfcpmkas2bN4sUXXyQWi/WrMFUsFrEsi8mTJ49AS9+czolHOLs8TNr1cJUiZhoD5mAIIYQQ4sQk06LECWXOnDlUVFSQSCQoFou9613kcjmy2SxTp06ltrZ2tJt5QtM0jahpELdMCSyEEEKINxkJLsQJpaysjLe85S2MHz+eQqFAIpEgkUiglOKUU05hyZIlvQu8CSGEEEKI4SXTosQJp7KykmuvvZb9+/eTSCQwDINx48YRjUZHu2lCCCGEEEetu7ubLVu2MHPmTOLx+Gg3pw8ZuRAnJE3TGDduHCeffDKzZ8+WwEIIIYQQJ4QHH3yQyZMnc+ONNzJhwgQefPBBAD7+8Y+jaVrvz/Tp00elfRJcCCGEEEIIcRxIJpN89KMf5amnnmLdunXcdddd3HLLLQCsWLGCv/zlL71TwletWjUqbZRpUUIIIYQQQhwHUqkU3/72t5k3bx4Ap59+Op2dnTiOw4YNG1i8eDGRSGRU2ygjF0IIIYQQQoyyVCrV56dYLPbbZ+LEibzvfe8DwLZt7rzzTq699lrWrVuH53nMnz+fYDDIW97yFvbu3TvSlwBIcCGEEEIIIcSomzhxIrFYrPfnjjvuGHTfNWvWUFdXx1//+le++93vsnHjRmbNmsXPf/5z1q5di2ma3HTTTSPY+lfJtCghhBBCCCFGWWNjY58CNH6/f9B9582bx6OPPsonP/lJbrzxRh566KHeEQ2A//mf/2HKlCmkUqkRL2ojwYUQQgghhBCjLBqNHnYgoGkaCxYs4Kc//SnTpk2ju7ub8vLy3u01NTV4nsf+/ftHPLiQaVFCCCGEEEIcB5YvX95bHQrA5/OhaRpf+tKX+OUvf9n7+PPPP4+u60ycOHHE2ygjF0IIIYQQQhwHZs6cyd13382MGTN461vfyhe+8AUuu+wyFixYwBe+8AVqa2txXZePfexjfPCDHyQUCo14GyW4EGKUtBVtlifSdJQcIqbO2eURpgX9aJo22k0TQgghxBhUX1/PQw89xCc+8Qk+85nPsHTpUn72s59RXV3Nhg0bePvb345hGLz//e/n9ttvH5U2akopNSpnHkGpVIpYLEYymZSVmsWoU0rx29YE9zV30G27oIFSEDF1Lq2M8fFJNfh0mbEohBDizWUs99cOtu2ZZz5IJOIb1mNnMiXOO+9nY/K6j4SMXAgxwv7eleYHe9vQNY1JQR+6pqGUottx+UNbgpCu89FJNaPdTCGEEEKIIZPbo0KMIE8pHmzpwlaKOr+FfmAKlKZpxC2TiKHzcEc37SV7lFsqhBBCCDF0ElwIMYL25EvszBWptAYeNKywTLodlzXp/Ai3TAghhBDi6ElwIcQIKimFpxTGIEnbB/9Bljxv5BolhBBCCDFMJLgQYgTV+y2ipkHadQfcnnU9/LrGxMDwJosJIYQQQowECS6EGEFR0+CSyihpx6P4utEJVynabIc54SBzI8FRaqEQQgghxJGTalFCjLD3jatkc7bAqlQOS9cIGzpFT5F1XSYF/Xxicq2sdSGEEEKI45IEF0KMsHLL5PaZE/hzezePtCfptB1ipsHba+NcVVPOeJkSJYQQQojj1JibFvXHP/6RqVOnYpom8+fPZ9OmTQCsX7+ehQsXEo/HueWWW3gTrP0nTmBlpsF19ZX89JQpPDh/Or+ZP41/bqiRwEIIIYQQx7UxFVzs2LGDG264ga997Ws0Nzczc+ZMbrzxRorFIldddRULFixgxYoVbNy4kfvuu2+0myvEUdM0jZChD1o9SgghhBDieDKmgotNmzbxta99jXe9613U1tbyL//yL6xatYpHHnmEZDLJt771LaZNm8btt9/Oj3/840GPUywWSaVSfX6EEEIIIYQQx9aYyrm48sor+/y+ZcsWZsyYwZo1a1i0aBGhUAiAefPmsXHjxkGPc8cdd/ClL33pmLZVCDH2lTyPlakcHSWHqGmwIBoiYhqj3SwhhBDihDWmgovXKpVK/Pd//zef+tSn2L59O1OmTOndpmkahmGQSCSIx+P9nvu5z32OT33qU72/p1IpJk6cOCLtFkKMDc8k0tzd2M6eQglXKTSg1mfx3nGVXFtTLhW5hBBCiGNgzAYXt956K+FwmBtvvJEvfOEL+P3+PtsDgQC5XG7A4MLv9/fbXwjx5rEimeX2nfvJOC51fgu/rmN7inbb5nt7WtGBt9X2/+xob29n8+bN7Nu3D03TmDhxIrNmzaKiomLkL0IIIYQ4Do3J4OKJJ57grrvu4oUXXsCyLCoqKli/fn2ffdLpND6fVNYRQvSllOL+fZ2kbJfJQV/vCIWla4zz+9hXKPGL/Z1cWhUlbLw6RWrTpk0888wzFAoFLMtCKUVraysbN27kwgsvZOrUqaN1SUIIIcRxY0wldAPs2rWL6667jrvuuouTTjoJgIULF/L888/32adYLMrdRCFEP3sLJTZm8lT5zAGnPlX7LFqLNqtSud7H2tvbeeaZZ3Bdl/KKCnyxcsKxcioqKigUCixbtkwKQwghhBCHYUyNXOTzea688kquueYarr32WjKZDADnn38+qVSKe++9lxtuuIHbb7+dSy65BMOQxEwh3qwSLfvY/vILNG1ch+e6VE+eyoyFZ5Opm4ijFD594JwKS9fwgIzj9T62efNmMsUijeMmszYYJa2b6EoxvZRlvunHa9vPtm3bWLBgwQhdnRBCCHF8GlPBxaOPPsrGjRvZuHEj99xzT+/ju3bt4kc/+hHXXXcdt9xyC7qus2zZstFrqBBiVDVtXM8zv/4ZuVQS0+9H0zS2v/wCe9asYsKlVxAon0zW9fDrfQdnlVIksjmKhSLbVrxEOBJg+vTp7G7exxPjprMnEkdHEfBcHE1jdTDGdl+YC7I5WltbR+lqhRBCiOPHmAourrnmmkFX3p48eTI7duxg5cqVLFq0iMrKyhFunRBiLMilkjz34C/IZ9OU19X3mfqUS3bT9NhfmPv2f+QZzyJmGr0LFHqeR3tHO/scRWUhC81bWe15bNiwgVVllewKRSn3bHwHP4MURDyXTsPH01UTWKQVR+NyhRBCiOPKmMu5OJS6ujquuOIKCSyEeBPbs+YVMt1dRKtq+uVUhGLl2KUiZ+5cx+Sgj935Ep22Q8H1aOxK0GQrQspjqZ2hMh6noqIC3bJYH4yB674aWBygATGnRNLy01FVP4JXKYQQQhyfxtTIhRDixOA6Nh1792CXipRVVBKrqRu2Y3fuawIFuj7wvRFfIADbN/G1a97BL/Z18kx3hvZikWyxyIxilnNLWSY4BaBnzRwtFCHvD+JzbGwNLMvqPZZSChwHPRBCVVUP2qZ0Ok1TUxOO4xCLxRg/frzkhAkhhHhTkuBCCDFslFJse+k5Nix7nFRHO57rYvkD1M+cxRlXvG1YggxdP3SnXXkemmHQEPTzuWnj6Cw5rNy2nZdXbWByNNJvtMPEw9Q1HMNAUx6lUql3H6UUpmURDIWJBAL9zuU4Di+88AKbN2+mUCgcaJ9OZWUlixcvpr5eRjuEEEK8uRxX06KEEGPbpqef5PmHfkWqvY1QNEq0qhrDMtmzZhV//8n/kupoP+pz1E6dhqbruI7Tb5tSCrtYZMKcub2PVfpMJpkaUac04PH8SjG5lKNoWlRVVVFRUUEoFCIUClFZWUmkuoaY38eCaLjfc5999llWr16NUor4gWlW4XCY9vZ2/va3v9HZ2XnU1yuEEEIcTyS4EEIMi3w6xdq//xVd14lW12D6/OiGQSAcIVZbS2L/PjY98+RRn6fh5FOJ148n1d7WJ8DwPI9URxvBsjJmnHk2rUWb3+zv4rt7WnkEP53ROIXiwEnZs7taCKPo0AzCZVFqamqorq6GUJiEqzgvXsaMkL/Pc7q6utiyZQuBQIBwOPzqYn2WRTweJ51Os27duqO+XiGEEOJ4ItOihBDDonHjOvKpFNGamn7bdN3AHwqxe/VKTn/L1VgDTDE6XFYgwAXvv4Hl999LYn9zzzQoTUOhCMfiLHr7e3jKCPPj9bvoclw4kKNdmjCTuq42ri4mCb9mDYxSqUR1Js2NMR+P+n00FXtGODwgpOtcVhXlU5Nr+02n2rt376CLeWqaht/vZ+fOnZx77rl98jjEiWF/scTKZI6i5zEh0DOyZQ6ytooQQryZSHAhhBgWhUwGtMFzIkyfH7tYopjPHVVwARCvH88VH/sMe9avoXXHNlzXoXL8RCafejqrlMld2/ehgMkBH7qmoZQiaUTZ48EjCcXF7XsxTRPbttE0jRkzZnDxGfO5TtN5OpFmX9HGr2ucEQ0zOxwYcKXvg7kZA20DMAwD13VxHEeCixNIwfX4YWM7f+tIknJcNA0MTWNq0M8nJtcyryw02k0UQohRJcGFEGJYBMJhUOB57oABhlMqYfp8+IPD0/myAgGmn3EW0884q/cxpRQPbW6k4HlMDr46jUnTNMr9Prx4jI6AD19AI5xJEYvFmDlzJlOmTME0TSzg8urywzp/JBIBeqZjDVS5qlQqEY1G8fl8R3WdYmz5/t5W/tDaTdQ0mBTsCV4Lrse2XIFbtzfz37MamPq6KXRCiBPHpk3nEwoN702EXC4H/GxYjzmaJLgQQgyLCSedQrAsSq67m0hF37VoPM+jlM8x/cxFRz1qcSjtJYfN2Txxc+CPtrhlknT9VJ91Lm+rjR/WMb2cTWFLgsK2BF7BxawMEJxTweRJkwmHw6TTaaLRaJ8RDMdxcByHWbNmSUnaE8jOXJHHOlOUWwZx69X3WMDQaQj42JUv8YfWBJ+aMnyll4UQ4ngjCd1CiGERisY45eKlBxKr23FKJTzPo5DN0N26n1htHSedf9GAzy16HuvSOV5JZmkr2kfcBlspPAXGIFPfNU1DO7Df4XC6CnQ9tJXUE3spNWVwuwsUNneR+L8d2M+1s+isRei6TiKRoFAoUCqVSKfTJJNJxo8fz9y5c9/4JEC6q8DeDZ3s3dhJNikrgY9VLyYzZB2PcrN/wKhrGlFTZ3kiTcH1RqF1QggxNsjIhRBi2Jy0+CJMn48Ny/9OuqMdz/Ow/H4mnzKfBVdcS/R1C9EppfhjWzcPtXaxr2DjAmFDZ3E8wj9OqKbaN7RchWqfSZXPpLVoExmgA5h3PQxNY1LgjacqKU+RfGwPdlseM+5HM169F+MVHPLrOphUM5GlS5eyevVq2tra8DyPQCDAvHnzmD9/PoE3GKUpZG1WPbqHpi3dlPIOoPAHLXynlBNcUEnAZzK3LNjnLrkYPTnXQ9MYNM/Gp2nYSlH0PAKG3LsTQrw5yTeWEGLYaJrGrLPPZ9oZi2jfsxOnVKKsooryuoEXk/vZvk7ube5AA6osE0PTSLsu/9eWZEeuxDdmTRhSx9qn61xRXc4PGtvIuR6h1wYESrG/ZDMnHOD0AdaseD17XwanJYsR9fUJLAD0gImXd8it7WDSe+cwadIk0uk0juMQiUQOK8/CLrk8+9A2WnYkCUQsYtUBug14qNxju5PEXZclXOaj0mfy1qoYN0yowj/IquRiZNT6LBTgKIU5QICRdT3qAz7CMhVOCPEmJsGFEGLYmZZF/fRZh9ynuVDiNy1dBHStzwhFhW5SZhhszOT5c3s3HxhXNaRz/0NtnI2ZPE8n0mhA2DCwlSLneowPWHx6ct1hlQy123Iox0O3Bu7Q60ETt7uIlylhxPxEo9EhtbNxYxetu1JEKvyYlkFGV/yqHpoCOhFb4Us6lId85DyP+/d30u04/MeU+kHvmotj7/x4hJ80m7QUbcb7rT5/i6LnUfQUb62KSUlaIcSbmtwGE0KMiqcTaZKOS+UAIxOWrhHQNR5pT6IOMz/ioKCh8/+mjePTU+qYEwliaBpxy+CD4yq5c3YDcyLB3n1zqRLJ9jylQv/Vvg+rE68BR9jZb9zYhQJMq+cu9+oyaA5AVQkiSkNTYGcdanwWFabJ450pNmYLR3QuMTxilsk/TazBp2vsLpRI2i5Z16WlaNNcsFkQC3FNTfloN1MIIUaVjFwIIUZFwnbR6EmEHUjQ0Ek6LnlPERosQ3sQAUPnmpo419TEcTyFoYGyPQpbEiS2dpFrz5NIl9hfcEloGlbQYtLcSmafXU8o2jOlyaoPo1k6quiiBfp/VHo5G6s+gh45sjUs8pkSpvnq/Z01ZWCq13woa+A6PYFV1NTpyNs8m0hz8muCIzHyllbFiJkGD7V0sT6TJ+9AzDR4d12cd9VXDpjrI4QQbyYSXAghRkXMNFCqJ6l7oFGCgqeotEwCRznFxNQ13KxN9593YjelcUoemUwJy1NM0jQq/Aa7iw4bn91H2+4Ui6+bRSjqw6wN4ZtYRmFHN5qhox2YHqWUwss5oGuETq1GO8L2hcv9dO7L9v6eMcB6bZEhBeaBc/ZUudLott0jfh3E8FlUHuGsWJi2kkPR86jyWX3ye4QQ4s1MPg2FEKPi3HiEMlOna4AOs6MUeddjaVV00JGNoUgvb8RuTKNHfSSLLkUFjt/ANXVitscEDaJVATqaM2x+fj/Q06GPXjoJ38QobrqE3ZHHSRRwO/PgKcIL6wjMqTjiNk2eW4Wua5QKPddf7kDpwCey6yo0HYJlPaMiSikUiiqf3A8aKzRNo9Zv0RD0S2AhhBCvId9UQohRMSno55qaOL/a34ld7BmlMDRIOx4dtsOMkJ+rDsxfdx2P5m0J9m9PgoLxs8oZPzN+WHkRTleB4s4kesikWHRxSu6BEQENTwdXKaIFh66ID1/AZM/6TuZeMB5fwMSI+Kj4hxkUd3ZT2JlEFV2MuJ/grArM2tBRJVePm1XOhDlx9q7vwi7qzAtb7K2FvKMwPUUo6sMf7AkuuhyXMtNgcUXZEZ9PCCGEGAkSXAghRpRXcHpWvN6a4Nq8jVGt89eYxz6vhKcgbOpcUBHh5oZaqn0WHU1pnrx/M4n9OVzXAwWrH99LvC7EpR8+mXjdocvKOu05VMFFrwzgdh9YoO41QYGra1iewud4+AIGxbxDIWPjO5BnoVk6gVkVBGYd+SjFQAxD5+y3TSNWFWTn6nZmNJeYGDDZG9MpD/gIlfspeB6dtoOj4Lr6CqYF/cPaBiGEEGK4SXAhhBgxbqpE9593YO/Pgq6hGRpXt3tcbGrsmhXFt6CGieEAU0I9nehMosijP95Asi2Pbmj4/AaKnpGMzuYsf/reGt7xHwsIRQ/R6X5NIKH3JoYreko9va59jodh6Fj+kUnKNS2DeRdOZPbZ9STb85znuvyfm+fJZIY220UH6v0W19bGubb28EZqhBBCiNEkwYUQYkQopUj9fQ/2vgxGeQDtNZWSyooOczckKasoI3xmrPfxzS/sJ9WexzA1TF9Ph18DdJ+Bayiy3UVWPbqXc98xY9DzWnVhtKCJl3Pwhyx0Q8N1FIbZ01E3PYWtaxRMjULCpmFuJcGyN14Ebzj5AibVE8uoBj5JOdfbDnsLJUxNY3rIL4vnCSGEOG7IN5YQYkQ47XlKjWn0iK9PYAGg+000Uye3vgNlv1oyaccrbSj16loQr2UYGmiwa20Hyht8LQwj6iM4K47KO2ieIlzuR3mqZ5TC89AVdPlNkp1FAhGL2YsGXk18JMUtk1PLQpwcCUpgIYQQ4rgi31pCiBHhtOVQtoc2yJQjLWjiZUo43a8uFGcX3AML1fXf/+DienbRxbG9/ju8RuT88fhnlONlbYJKEQuZBD0FtkeLgsaSR7Q6yNnXTqe6QZKmhRBCiCMl06KEECPjjdIFDg4+vCavIFzuJ93Vd1Vqz/VwbA/PVSgPHNujdU+K8TPKB81J0P0m5VdOo7QnRWFbgkDOxguapCyDmojF5PIA9dNiGKbcbxFCCCGOhgQXQogRYdVH0PxGz/SkUP9Vrb28jVkRwIy/mpw9++w6WnensEselk/HtT3skoujQVvMwNOgVmk8++A25i4ez0nnjRs0wNAMDf/UGP6pr+Z0VA7/ZQpxQnM8xXPdGR7rTLI3X6LcMrmwooyLKqNEZXVyIQQSXAghRohZEcA/NUZhYxeYOvqBBG2lFCrvgEfPitevWZBs1ll1bHmxhf3bkxQdF8+DdQ0+1jf4SIV00DUihs6MhEfm+X3UTIrKtCYhjpGS5/H1nS38vSuFqxQBXWd3vsjKVJa/tHdz24wJ1Pn73zgQQry5SHAhhBgx0QsbUAWX4p4UXqoEugaeh+YzCJ1eQ/CU6t59lVJ0Ne+lftJ2uvdvI9XusmL2fF6ZOQ5DQZkNfp9OQdNYXW3Q6lNMXdvGhRJciKOklGJFKsff2pNszhUI6BrnxctYWhWl3j+ylcTGkodaE/ytM0m1ZRJ5zSiF7Sk2ZPJ8a3cLX585QUomC/EmJ8GFEGLE6EGT8qunUdydpLi9Gy/vYJQHCMyMY40L93ZKlFKs/ttf2LDsMUqFArphkIqYrJts4XOSVBhR9ICFBlguBF3YF9F5NJXhwtG9RHGcU0pxT1M7D7R0UfAUQV3HVYpNmQJ/ae/mP6eN45Sy0Gg3c8QVXI8/t3Xj17U+gQWApWvU+CxWpXJszRWZFQ6MUiuFEGOBBBdCiBGlmTqB6XEC0+OD7rPzlZdY+/e/Yvp8lNfVo2kaL1q1lPxBylNd2JaN36zr3d8EDA9WlfV0DuXO6fFFKYXTWcDLlNB8BlZtGM0Ynb/hU4kMv97fRdDQqfe/+hXpKcWefImv7dzPPXOnEDLeXMn/+4o2bSWH2CB5FRFDp8N22JYtSHAhxAjo7u5my5YtzJw5k3h88O/T0fDm+nQUQox5yvPY/OxTeJ5LKBrrDRRK4Z67xbpm4Dl5PLf46nMU+FyFHTYoqcHXvBgLXMcjnynh2O5oN2VMsFuzdP9+O12/2kTid9tJPLiVzl9torAtMSrt+Ut7N7ZSVFh9773pmsaEgI+9hRLPJtKj0rbRpGtvXPAN1bOfEOLYevDBB5k8eTI33ngjEyZM4MEHHwRg/fr1LFy4kHg8zi233NJbsn2kSXAhhBhTssluOpub8Vw/rbvTtOxM0tGUIWCXQNPwlI5SCs/pKVGrlMIpubiWTmXUj2+MjlrkUiVWP76XP31vNX/+/hr+9N01rPzr7n6ldt9M7PYc3f+3g+LOJJplYJT70YImTluO5F93k9/cNaLtKXkemzJ5yoyB785buoYHbM8VB9x+uNpLNo91JHm4vZv16dyodQCGYoLfx/iARWKQoDjpuIRNnZMjwRFumRBvLslkko9+9KM89dRTrFu3jrvuuotbbrmFYrHIVVddxYIFC1ixYgUbN27kvvvuG5U2yrQoIcSYkmzPkerI4zkGhqWBplHMOdRu34u/fgrFgB9/3sZ1PKBnkT3NZ2DELC6vi4/JKVGZRJGnfr2FrpYsPr+B6TNwSi6bnttP89ZuFr97JuW1b655/EopUk82YnfkMeIBNL+BpmloPgO9IoiTKJB5bh+BaeVo1sjcB9PR0NBQHGJRxqO4O19wPX7c1M4jHUm6HRcUBAyNk8JBPjG5jqkh/xsfZJSYusbbauLcubuVhO1Qbhq9/9byrken7bK0Ksqk4Ni9BiFOBKlUim9/+9vMmzcPgNNPP53Ozk4eeeQRkskk3/rWtwiFQtx+++3cfPPN3HDDDSPeRhm5EEKMGcpTbHiqC4ig6QUMS8cwNUyfTnU+w+zduykGfOTKyvDHIoQr/Jg1QXI1PubEQlxRHRvwuF7BwcvZKG907hCvXdZI1/4sscoA4Zgff9AkFPURqw6S6syz6rE9x8Xd6+FS3Jui8xebyK/rwMs6OPuz2PuzeDm7dx+jzIfbXaS4NzVi7TJ1jTNiIVKON+Dfo+B6mLp2RHfnlVJ8d08rv2rpwlWKhoCPyUEfZYbBylSOL2xrYn+xNByXccxcVVPOu+riFD3FrnyJvfkSu/JF2ksOZ5eH+bdJtaPdRCGOa6lUqs9Psdh/lHTixIm8733vA8C2be68806uvfZa1qxZw6JFiwiFem5UzZs3j40bN45o+w+SkQsh3uSUUiRacuzb1k2p4BCO+pgwp4JwbOTvQLY3punanydWN4/ufctxnQKG2ZMcqukaizatxVdIsGve6RQqI+SAsKFzUTTMzQ01VPv61tgv7k6SW9NOqSkDKMzyAMFTqgieXDViCcPZ7iL7tnQTDFvor0sC1nWNUJmPtj1pultzxOvCI9Km0VTcmyL555246Z6OtGZpgIYqONhtLlZNCD1koZk6KIWXc0a0fVfWlPNsd4b9JZs6n4V+MOfH82gulpgbCXFWLDLk427LFXm8M0XcNCh/TT5HxDQIGjp78iX+3NbNRybWDNu1DDdD0/hoQw0XVJTxZFea5kKJmGVwXnkZZ5WH8elyv1KIozFx4sQ+v99666188YtfHHDfNWvWcNFFF+Hz+di0aRO33XYbU6ZM6d2uaRqGYZBIJEY84VuCCyHexBzbZeUje9i9rgOn5AEK0Fj/VDNzL5jAzDNrh32aUdHzeKE7y8pklpJSTA76uLAiSq3forsth+co4vWn4JU6SXduwLZzaLqJUi54Hgt3m3z+H6bSWV+F4ykagr4Bp2Lk1raTXtaIsj30oAmajt2Ww358L/b+LNFLJo1IgJFJFLFLLuHYwOsj+AIG+XSJTFfxhA8ulFJknt/XU4K4IoCXd0D1rJ6uNB0cDydRxAqa4CrQtJ6/3YHndrfswy4WCZfHCZcfmy/L06NhPtZQyw8a29idL2Fo4NGTzHxSJMgXptVjHcG8qOe7M2Rdjxpf//eBoWmEDJ1HO1PcOKF6TE7tO0jTNOaWhZj7JizHK8Sx1tjYSDQa7f3d7x/8Jt+8efN49NFH+eQnP8mNN97ItGnT+u0fCATI5XISXAghRs6avzeyfWUbgYhJOOZD0zSUp3qSjx/bSyBsMWlu5bCdb3+xxJe372NDJt/TdwQUil80dvCxcVVM1TR6JqPoVE66kFD5VNKdm7ALCXTDjz8yFSs0jfraOmaWD17u0k0WST/TDArMylensOhBE6/gkN/YiW9ylOCsimG7tsEYlo6ma3ieYqA0Yc9VaLqOMUJ5BaPJac3htObQIz50y+j5e2RsMHo6rcrQUSUXVXTxCg5GzId/Uhl7169l/ZOP0tnUiOe5mD4/DSfP49TLLidaVf3GJx6iK2vKmVcW5MmuNDtyBQK6zhmxMOfGI4QHSfZ+IxnXQ9MYNHDw6Rp516PoKQKjVIZXCDG6otFon+DiUDRNY8GCBfz0pz9l2rRp3HHHHaxfv77PPul0Gt8ANzSONQkuxAlPKUVJKQw0TKmT2CubLLJrbQf+oEEg9Op0Ik3XCJf7SXbk2fLifhpOqkAbhtfN9hS3bd/HmnSe8QGLgKbhpkrY6RItKL7RvpcvpC2qTMhkbQIRH6HyKYTKXx3mTXbkqRxXRiR+6Clbha0JVM7BqOwfgOgBEy9rU9jQOSLBRbw+RLQyQLI9j1XZv2OaT9uEYz6qx9DK4oWsjWt7BMLWsAY9Xt5BOR56pOeYRrkfr+CiSi6YOmg9eTdOdxEjZBJZNI5d61/huQd+QalQIByLoZsWdiHP1hefpaNxN5fceDNllVXD1saDGoJ+PjR++KYGVlkmqJ71MvQBAoyc6zEp6MMvn1FCiENYvnw5f/7zn/nmN78JgM/Xc2Nwzpw53HPPPb377dq1i2KxSEXFsf+ee70T/1aZeNNyPMUj7d3866a9vH3Vdt6+ejvf3Lmfzdn8aDdtTGjbnaKYdQiErQG3B8MW3a35YSuVuiKVZWO2wDh/T2Bht+dxOvNoJUWdq5E2NB5XJWYoRTRlUyq8WvJSqZ7RFF3XDmuqlpPoafNg+2l+A7tjZN4HhqEz++x6NA0y3UW8A0nlB0eIXNdj1ll1WP4juyM+nNr2pHj6ga38+ftr+MsP1vKX/1nD+uVNlPLDk/egB000U0fZPdWYdL+JVRdCC5rgKZTtglKYcT/RyyZjTgvzyl/+iGvblNfW4QuGMC2LYFmU8to6Evub2bDs8WFp27G2uCJCzDJoL/V/LYueh60Ub60qH9NTooQQo2/mzJncfffd3H333TQ2NvL5z3+eyy67jMsvv5xUKsW9994LwO23384ll1yCcYSjrUdDggtxQnI8xX/t3s/XdrawLp1D0fMF/oe2bj6zuZFn3oSLYL2e6xyohjNIX0YzeqZI9ZR8PXpr0zlsTxE0dNysfWA6jI5m6ei6RgR4pVzHH/Mzxa9BpkSiLUd3W55kewE0jXkXTTysaVqa7w0+TF01YuVNAaacWsVplzZg+QxSHQWS7XmSHQU0Q+OUCyYw66y6Nz7IMda0JcFTv97K3g2daIDl08lnbNY80cTTv9k6LAGGWRPCrAnhpUu91Zh0v4lvXBizPoQRtvBPiVH14bkEZ1fQvHkD6a5OwvGKfp1u3TDwh8LsWrOSYi571G071ur9Pj44rgpHKfbmi2Qcl7zr0Va0aSrYnB4dvNqZEEIcVF9fz0MPPcR3vvMdTj75ZHK5HD/72c8wTZMf/ehH/Ou//itVVVX88Y9/5Otf//qotFGmRYkT0mOdSR5pTxG3DMrMVzuaVZaisVDi27tbmVcWImqO/t3i0RKtCmBYOk7JG/CueSnv4A+ZhMuHZ2qI85rKnt7BSkGvmQJiqAOJs1GLsO1x1uwKWiyDUsEhUu5n4kmVxKoPrwSovyFKblUbXslFf12goTyFsj0CM0cuwU3TNGYtqqdhbiXNW7opZEr4QibjZ8ZHpSrX69lFl1f+todSwSVWHeztyPsCJo7t0bIrxdaXW5m7ePxRnUfTNSJnjyP5l524XQX0Ml/PSIbjobIOepmf6MUN6FbP3yyX7ilDa5gDf1WZfj+lfJ5CJo0/NPaT4d9VFyduGTzUkmBXvoinFFHL4G21cd43rpLIm/jzSAhx+C699FI2bNjQ7/Grr76aHTt2sHLlShYtWkRl5fDlTA6FBBfihKOU4s/tSRSqT2ABPZ28cQEfTYUSy7vSXFVTPjqNHAOqJ5ZROS5M2+400eoA+ms6+o7tUiq6zDyzDl9geD4mpgZ96FpP7oWyvX4jJmkdFhQ1fJqOo0HI0jl96aQjOpdvUhTfxDJKu1NQZqH5ehb8Uo6H213EKPcTPHl45+l7rkfr7jTprjyGqVM7OdYvNyQY8TF9wdgrNdq8NUGmq0Ak7u83QmBaOqals3N1O3POqccwj27Exz8pSuyKqWSe24fTlsNzPDRTx6qPEDmnHv/kV+/e+4KhnnK0ros+wNC+a9sYptmz33FA0zQuq4pxcWWUvfkStlLU+61+n1NCCHGk6urquOKKK0a1DRJciBNOSSn25IuDfmGbmoZS0FgY2wtWHWuarnHG5ZN5+oFtpNrzWH4Dw9SxSy6uo6ifGuOkc8cN2/nOj5cxzt9Jc7FEvQ68mlJBUleYwKW5Ax1X1ZMXcaQ0QyP21imkHt1NqTHdMwULQNMwa4JEL52MOUwjMgAdTRlW/GUXidZcz8iIAn/QZMqpVZx6yURMa2x3HnPJnn8LgwUOVsCgmLUp5hxC0aOvPOI/EPzZrTm8nI0esrBqQ/0KB4yffRLBaIxcKkkk3jcpUSlFIZNh+sJFBMsOr7rKWGFoGlPG8GrcQghxNCS4ECccAw1D0yh4g+cKKK2n9OObXbwuzIXvn82OV9rYs74Tp+QSrQoy9bRqps6vHrZRC+hZLOw/ptRx2859NIYcfBkb01DkNLCAq7I65xQ0vJILhoZ/8tF1GI2wRfnbpmPvz2I3ZVCewqwK4J8c61mgbZgk2/M88+A2st1FwuU+LJ+B8hSFrM3mF1pwHI8zr5wyphN1DUtHqZ4O+0Dt7CmXq2EOY56Kpmv46g89lSkYKWPuhZey8s+/J93ZQShWjmGa2IUCme4uwuVxTr7gkmFr02hpL9k80ZlmczaPrmnMLwtxQUXZm3raphDi+CXBhTjhmLrGeeUR/tDWTbXVv7OUdV38msbpsggUAGUVAeZf0sCpF03EdT0MUz9mHeHTY2G+PbuBR5q6+PvWNopFl1NdnUuKBmcU6FnfIFPCP7Uc38SjvxutaRq+cRF844a+ovLh2railUyiQHl1sPfOu6ZrBMt8aLrNnnWdzDqzjvLasft+q58WwxcwKGQdgpG+1cOUUhRzNlNOrcYXHPmvjJMvuBjdMNiw7HHSnR0o5WFaPmqnTOOMq95O5YSJb3yQMeyZRJr/3tVCW8lB10ApeLQjya/3d/H/ptczO3x4eUZCCDFWSHAhTkhX1ZazPJGmsVBiXMCHeaCznHVdWoo2Z5dHmB8du5290aDpGqbe906p4ymWJdI80t7NjnyRoK6zJF7GW6tjNAywKvbhmBT0888z6vnHWIzUY3uw23PgOngKNJ+Of1YFsYsbRmT17KPlOh6NG7vwB80B1wLxh0yS7QX2bese08FFtCrI5FOr2PpiKwCBsImmabiORyZRJBC2mHlmLQBeyaWwuYvCpi7cdAk9YhGcU0lgdhzdP/xfKZqmcdL5FzJ94SJatm/DLhaIxCuomTwVTT++Cx7uyhX5xq4WkrbD5KCvd/0Lx1PsLhS5bft+fnDyJBnBEEIcVyS4ECek2eEgn5taz527W2kqlFCAAvyaxjnlZXxuav2AC1mJVzme4r93t/BIRxJPKUKGTsbx+Nm+Dh7rTPGf08cx7yhGf6yaEBXvmU2pMYXTUQBDwzc+gvmaakVjnet4uI6HPkggdPA67KI74Pax5LRLG9A02LWmg2R7gYN/grLKAAveOpmqCWV4OZvuP++k1JgGXUMzddx0Cbs5Q2ZdC6V5OkbIpGrCJKzA4CuoHwlfIEjD3HnDeszR9teOJJ0lhylBX5/3vKlrNAR87C0U3/SFJ4QQxx8JLsQJ69x4GaeUhXiqK83eQgmf3jMVan40JIHFYXikI8nDHUnKTaPPnVNPmewtlPjGrhZ+dPJkAsaR3z3WDA3/5FifCkFHY2++yL6ijV/XOSkSwH+M72xbPoNgxCLdVRhwMcKDC+aFYkefBH2smZbBGW+dwqyz6mnZkcQuuZTFA9TPiPUmpKef3UdxdxIt0LMYnu438JRHsqUF1ru0rNnDLrWeUKycWecsZu6SSwas8jScbE9R8jxCxrGbznesvJjMEDC0Adt9sPDE6nROggshxHFFggtxQouaBlfKF/OQ9ZTz7Qal+k3J0DWNcX6Lvfkiz3dnuLBy9Cv17M0X+WFTOyuSWXKuh6lp1Pst3l1fwdXVx27VY03XmHpaNa/8bS+O7fapCqWUIttdJFhmMXF2xSGOMraUVQQoq+g/6lBqy5F9qQWv4KAdrL6la+SdDNliFz4jSLU1ga5AO+lMByv/8gdyqW7Oetu7jsnrvzOZ4/fbW3kql8fWNWoifi6vLeeK6hjhUViR9ki4CvTBVrEENA1cpQbdLoQQY5EEF0KIftKuR2OhNOhcb5+u4wG78kUuHNmm9bO/WOL/29bMjlyBSsukMuDD9hT7izbf3t1K1vV4b/3QFxLySiWKmzbh5fOYNTX4pvSv+JTqyAMQjFgkWnMEwxb+kIXnKvJZG8tncOrFE/EFDVp2Jkl15NENndop0QE78GOVV3To/tMOvJwNpoZ24H3hFkuYtknEjFPU85jKJKzHUBWKfCbNthefY/oZi6iaeGTrlQxEKcXKVfv40v52WnVFxFVYSmNHush3Ujle7M7w5enjCY9SnkLJ83ihO8vyrjRdjkOdz+KiyiinR0MYr3v/zIsE2ZkrDlily1MKD5gdPn7eJ0IIARJcCCEGYGjwuqUo+lBKgaI3UX40/aE1wY5cgclBf2/nzTA0Jhg+Wos2v9zXyaWVUap9/actDUQpRfrRx+j+7W9xWvajHBc9EMA/Zw6VH74B/7RpOCWXVx7dw571nZTyLkopPFeRSRSxSx6+oEnd1BizzqojGLF47CcbSbRk8dyeNTB8QZNJcys47bJJWL6xf5e9sCWB257vSVp/zTQ4x7NRKHzKj6Ocnge1njvtgXCE7pb97F2/ZliDi9S6du7c20a7Hya5GrqmA4pYwaNg27xAigfLQlw/fngXSTwcacflKzv28UJ3FheFpWmUlOJvHUkurYzy6Sl1+F4zVe8t1TEe70rRVnKo8Zm9AYZSiqZCiRrL5KKK0R8ZFEKIoZDgQgjRT9gwOC0a4snONBWmMUA5Xw+/rnHqKFfcKnoej3WmKDOMfneFAap8PfkhzyYyvK02fljHTP3lYTrvuQeUwqisQrMsvFyO3MqV2Puaqbv1VlatcdnxSjv+kEmsOoCmaTi2RyZRwBc0Oefa6YybWU66q8CTP99MJlEgXO7vWQNDKQpZh20vt+HaHoveNm3M5woUNneBpff8uArMA51gT/UGEz7lp2jkyRkZ4EAyuwa5VHLY2qFsl+fXtbA7DnVKezV3StPQLIOA7eLLuzzS3s176iqOKh/oSPxvYxvPJDLU+S1Crzl3ynH5S0eSCUEfHxj3atBzSlmIj0yo5p6mdnblS4QMHQ9FwVVU+kw+PaWOGv/hBcVCCDFWSHAhhBjQtTVxXkpm2Ve0qfdbvR25nOvRWrI5N17GvMjo1uDPOB55Vw3aiTQ0DQ3odg6vWpObTtP9m9+ApmHV1796nEgEPRSitGcPTQ88wl7nDAJhs08St2npxKqDJNvyNG9LMH5WnB2vtJHuet0aGJpGMGKh6xp7N3Yx88w6Kscfu3U4hoObsdEtA61Mw0kUwPV6qkVpGp7ngQaGMkhYbXhaz2t9cHQrGBm+O++lxgxNJQfP0PE7AwRkhk646LEvV+Th9m6mhQOcHA5ivqZMcKqjnV2rVtC+Zxe6oVM/fRaT5y846lW+d+YK/L41Qcb1KBU8woZBlWUSNHSipkHW9fhTWzf/UBvvkxPyjroKZoUD/LU9ydpMHlPTODMW5i1VMVnFWwhxXJLgQggxoNNjYT4xqZb/2dvG7nwJ6Cnn69M1zoxF+OyU+lG/4x4xdYKGRtbxBswPcZVCAeWHOf8+t2IFTmcn1rhx/bZpuo4Ri9G0vgV7UolQbf/VpTVNwxc0adqU4LRLHfas78IXMAZcA8MXNMhnSuzf3j3mgwsj6sPtLmDEAyjHw8vYKNvD0C00T0NDI2OkaPfv631OMZvF8geGtXysV3KxDlTgUii01yVDpwzYbUKh5PCN3S2EdJ0pIT83jK/ivHgZu1ev5Pnf/Zp8KoVuGCjlsXvtKjY89SSL33c9NZOnHlG79uSL/NumvTQWSliaThboxGF/scSkgJ9av0XcNOgoOezKFZn7uhLOp5SFOOUNyjq359pJFpOU+cqoDdceUTuFEGIkSHAhhBjUW6vLOT0aZllXmqZCEZ+uszAW5oxouM/d4DeilGJjtsCmTB5FT5Lq3MjRr2fh13UurYzy832dVCqz39SojpJDuWlwbvzwOu/5jhQpXw0+s4IIaXT6VurRAwEcV0N5/RNwDzJMDc9VlAouru1iDDKqcvD5peNgDYzg7ApKe1PgeJhVQVSZDzdro9su+VSBglNih74WV7ko1yOfTuGUisw6+3yqGiYPWzuMiMW8gkbYhaQO5d6r21K6YocFBQ3ihs7UgI+Sgq3ZAl/ZsY+Pxyy6H/oVdiFPed2rgbHnuSTb2njql/dx5cf/nUBkaIFe0fO4bcc+mgolTDRChgZoPSubK8XufJGAoRPQNY6k7tPO5E4e2voQq9pWUXJL+Awf86rm8Y6Z72BGfMYRHPGNOZ7i5VSWF7sz5FyP8QEfF1aUHfHCmUKcSC62T6GsNLw3hNJ2ZliPN9okuBBCHFLtgZKuR6q1aPPNXftZnc5R8BQaPaMfp0RC/PuUOsYFjm4NiLfVxnk2kWVnvkCVZRIxDWyl6Cw5eMD19VVvmMxdyNqsX97ErjVl5Cregq5MQlqOCeyhnube++NeoUDQX0QzdDxXDbh4XqngEqkIEIhYhGJ+ultzBCKDrYGhEY6N/Q5bYFacwtYEhZ3d6AETPWhixvx4OZugUU7W2E+6I0GpLYem64SiMeZeeCnzLl46rKNb1rgIDeVBzkvk+GuVgY6izAMNjWYTCprCr2k0hPwYuk4QmBTwsadQ4p5d+7g8m6Gytq5Pm3TdIFZdQ6q9jd1rX2H2OYuH1KYXu7NsyxZoCPrIugVs1ZPIrWkafiDvebQVbcpMg0rLZPIQOug7undwx4t30JJrIe6PE/FHKLgFnt33LFsTW/mPM/+D2RWzh9TeN5KwHb6yYx8rUzlKnod+YL2N3+zv4voJVbyzNj7qI5ZCiLFNggshxDGTdV2+uL2ZNekctT6LOp9+4HGPl5JZ/nN7M9+e3UDkwLQlpRQ78kX25nsWPTy1LETZG0xpqvf7uH3meH7Y2M6KVJZEoYRxYC2Od9VVcM0brHNSyjs8/cBWWnel8AfDBIxmvFKBrL+MLZxEET9T2IlyXdxkkolvnUiLFyDTXaSswt+no+XYLq6rmDq/GsPQmTq/mhWP7MYpuZivqwqVS5YIRCwmzhn7a2BolkHs8ikYz++nsKULN9UzTc6I+Agvqqd+wRlM6T6X7pZ9aIZBdcMU/KHhT/bXdI2yxRO44eEdlDSX58sNEiY4KLp1hV9pTA35iVuvfrVpmkatz2KX7dJWVU/VAP1i3TBQQNuuHUMOLtZn8jgKoqZJhWXSVnIwdIV+IMAwNY1O20EH3lNX0ftefyNKKe7fdD8tuRYayhoOVMUCv+kn6ouyN72Xn234GV8976vD1tlXSvFfu1p4oTvbJyndU4r2ksPdjW3U+iwuqCgblvMJIU5MElwIIY6ZZxIZ1mfyTAz4+pTgjJgGfl1nS7bAk11prqopZ0++yPf3trEmlSPreRhAlc/ibTXlvLe+8pDTsBqCfr46cwJ7elfo1jgpHDysakE7V7fTtidNtCKAYenY4+op7d2Dke+i5CujUZ9EVWY7/s69+CZMoPod1zA/4eflP+8i2V4gEDbRDY1S3sWxPeqnxZi+oAaAqfOr2betm33bujF9Ov6giecp8hkb09KZd+EEQtHhW73bKbk0bupi78Yu8pkSkfIAk+ZWMm5m+aDTsw6XHjCJXjiRyFl12B09ZWnNmhD6gaCprLKKsspjX/7VN7GM+qum85mX97OlKcMrQcXOoM7jcZ3p0QChQP9RooCu4Wg6BcsPqjjgcTXoSU4fIu81i9xNDvopKUXSdgGFpvWsIK5pcHFllPeNO/z1VhrTjWzs3EhloLI3sOhtq6ZRFaxiW/c2tndvH7bpUVtyBV5KZqnymX2qXemaRq3fYne+yO9bEyyOR2T0QggxqCEHF21tbdTU1PR5bPXq1Tz//PP8y7/8y7A1TAhxfFBK4ThpwMM0Y306Hc8k0gB9AouDLL0nHfeprjQLY2E+v7WZ3fkCVT6LGp+Jo6DTdvhRUztZ1+NfGmr6HeP1JgX9TBrCtBOlFDtXt2MYGobV00azphrN0LH378fKZ8gZMTqoYea5E6j44AfxTRjP5AkQCFtsebGF9j1pbMcjGPUxdX41M8+sxRfo+Wi1/AbnvmM6W15oYeeadgoZG03XqJ0cZdaiumFdubuQtXn2oW207koBYJg6nc1Z9m7souGkCha9bWqfFcSPlB6y8Dccm/Ko2e4ETZs2UCrkCJfHmTjnFKxA/0XkfOMiVFwzgzOTRc4oOOwyPNbu2oc7SACV9xRBy8KXTaOCVv8F6zwPpRQ1k6YMuc0zwgH0A0GEpWvMDgfosh06bYeSpyhoHotiEf5z+nisIeQpdRW6KDpF4v6BSygHzSCd+U46C53MYHiCi3XpPHnXo9Y3cNcgbhpsyRbotF2qBtlHCCGG/OlQX19PIpEgGn21bF9ZWRn//u//LsGFEG8iSim6Es/S2vpnstltgCIYnERtzeVUVV2MpumkHPeQC+1Zmkbadflja4Jd+WKfhfAsDer8Fp0lhz+2JbiiOjbsCaWep8inS32mLGmAWVmJUVGBl81S6rIJnP5O6t7dt+pR3dQYdVNj5DMlXFsRjFi9Acpr+QImpyyZwJxz6smlSuiGTrjcN2x3fne0Z1i2uY3nX95HvqPAnGiIOeEggQMBXangsGd9B9GqAPMunDgs5xxunuuy+tGH2fzscorZTM8fAY2yikoWXHktU+YvGPB5RsyPEfMzQylObg/ycjJLxND7vLZKKdpKNjNjESbZeTKFNJF4RZ8F61IdbUQqKpk8yHkO5bx4hIkBH3sLJSYFfBiaRrXPotrX894teh4fmVg9pMACIGSFMHWTklvC1Pt/VZfcEqZhErGGL7HUVQo0Bn1v6pqGp7ye/YQQYhBDDi6U6l8lJZ1O4/eP/aREIcTw2bf/AZqafo7n2VhmFNDIZDaRyWwim9vJpIabmBzwsyKZG/BzA6CoFA0BH493pQgb+oAL4cUtg935Es92Z4Y9uNB1DV/AJHcgh+C1NE1DD4fRcwVCdYOPMAQjhzetyfQZRKuGb10QpRS/e6WZnz2/m+6sTSFZRAHbM2lWFPP8Q3k5laaJL2BiF1x2ru5g9tn1vaMqY8nax//K2scfwfT7idXUouk6ruOQSXTy7AP3YwUCTJh98qDP1zSND0+oYle+yO58iSpfz/oSBdej3XaImgY3T5tI+VX/wIo//Y7ulv2YPh9KKVzbJlwe59x3vZ9QNDbktocNg89NrefLO/axK1/Cr/fkWeRcj6Ch84FxlZxbPvQAYFpsGpOjk9nWvY2g2b+yWmehkwllE5hVMWvIxx7M1KAfS9PIH2j76yUdl0lBH5XWwO+houexOpUj6bjELZNTy4IDjloKIU5sh/0tM2XKFLQDCWqnnHIK+oEPDKUU+/bt44477jhmjRRCjC3Z7Haam3+FplmEgq8uNmdZMWy7m9bWP1EeW8BFVSfxl44kCcel4nUdkm7bwa9pnBeP8Gx3hsAgd3Z1TUPXIH2YC+ENhaZpTDm1itV/bxyw+lMx52D5DcbPOrzVvUfSS7u6+Mmzu9A1qA9aJJI2ls/AUYp9ts3/JZN8sKICQ9Pwh03y6RLJ9jzVE8dWMm4+nWLTs8sxfT7CsfLexw3TJFpVQ7K1hfVPPsb4WScdcrRnXlmIL88Yz71NHWzI5OmynQNFAYJ8aFwVZ5ZH4OzzqJwwkR0rX6Rt1w50w2DcrJOYtuBMolVvPO1uMKeUhfjunAYe60jxbCJDwfOYZJqcGwlzfnXsiEapDN3gHbPewbdXfpt92X1UB6vxGT5s16Y9346lW7xz5jux9OGbonZ6NMyMUIANmTyTgr4+wX7GcXGV4qrqckxdI+u6bMkWcBVMDvpYk85zX3MHjYUSrlKYmsYEv8n76iNcWlU14OiLEOLEdNj/2u+77z6UUlx88cXcddddRA7UAtd1nalTpzJ+/Phj1kghxNjS0bEMx8kQCk7ut82yyrHz3bR3PMG8aQt4e22c37R0kXGKlFsmGpCwewKFa2vLOac8Qsw0aC3ZlA9wLu/AQnivD06Gy9TTatizvpNEa45Q1IcvYKAUFDI2paLLzDNqKK8d/spHR+sv6/ZTsF0mV4bJp18deTE1jXLDZJ9ts6tUYvprRpXHYg7u/m1bKKRTRKv7d+41TSMYjdKxdzfpzg6iVdWHPNapZSHunD2RHfkiXSWHmGUyM9S3olfVxElUTZw07NdR7/fxwfFVnKYsfvtKE6sbu3nF8/hpwOLi2TW8fcEEykNDS95fVL+Ifz3tX/nlpl+yL7MPT3nomk5tuJZ3z3o3iycMrbLVGzF1jVum1HHr9mb2HBiFsfSeURgdWFoV463VMX65v5M/tCZoKzl4SuEBKdslahqMC1h4XpF92XaeTzu82G4znxW8p+Eklk5eSsQ3theMFEIcvcP+tr7ggguAnpGK888/v0/OhRDizSVf2IuumYPPzdYD5HO70DSNf5pYzaSgjz+0drOn0FOpZ0bYz9U1ca6ojmFoGkurYtzd1N6bFPtaHSWHmGlwfvzY3HEPRX2c/56ZvPLXPbTtSR/oqGsEyyzmnlnH3AvGj7nKOAXbZX1zkuiBykhWwETXNVzXwzB0fJqGq6C5ZDPd76eQcQhGfcRqxl6Q5JSKoPWUgx2Ibph4XqFnv8OgaRrTQwEYhUt9fkcn3/zbZrpzNvGQRcBnksrb3P/iXtY0JfnyNScPOcA4b/x5LKxbyNr2tSSLSaK+KPOq5xEw+ye6D4cZ4QB3zm7g0c4kT3Smybou8yIh3lIdY3F5hB82tfNASxc+XafGZ6IpWJ3JkXMVugYFx2Z3cgdFt4hfMynqcTa5U7l3w09Z2bqSz571WaI+6T8IcSIb8q3A/fv3S2AhxDFQKnWSyWxGKY9weBqBwLjRbtKgDD2IYvBpSko5GEYY6JnWdHl1OW+pitFWclAoanxWnykXV9eU80wiw4ZMjnLTpMzUe6tFacCH6quo9R+bCkUA0cogF7x3Ft2tOZLteQxTp7qhjEC4/zlTjsuyrjSrU1lcYFY4wMUV0WPavtc7OJpz8CU0LZ1gmY9sdxFdU2gHVoNWQDHv4Nge00+vwfIdfbWo4RaOV6LrBnaxiDVA7l6pkMcXCBAuP/Kpacm8zfM7OunKlgj7Dc6aUkldbHg75wXb5X+X7yBdcJhcGeoNSMN+k5Ljsb45ye9eaebD5w29IpXf8LOwbuGwtvdQav0WHxhXxQfG9S0tvDVb4P/auikzjd6RxJTj4ioIGRopx2VbqQPllnryRNAwKFIya4mET2Jtx1r+sO0PfPDkD47YtQghRt6Qgwtd1/niF79IY2Mj6nUVI37yk58MW8OEeLNw3Rx7G39KZ8cT2E4SlIdpllEeX8Skho/g8429RdbK42fS0bkMzyuh66/eie30QrxcmsCO4lzigVM5t62bJRVlRE0DXdOoG6QDHrdMbp85np80dfB0Ik1rycGgZy73O2oruKJ66Im2Q6VpGvG6MPG68KD7bM7muW37/p4RGNXTuX+8I8UD+7v4zJQ6zjtGoyuvF7QMptdEWLW3m/iBO+Gx6iCu41HM2tiqJ7Qoy3qUfA7TTq9m9tn1hz7oEVBK0ZoqUnRcKiN+Iv6hT12rnz6T8vrxdDXtJfa61bNdx6GUyzL9govxhwb/uxzKX9e3cN9zu2hPF3vWsgCigd1cfeo4Pnj2JMyjXP/joJd2dbGvO09dNNBvpMtn6oR8Bo9vauW9ZzUQOMqSwEopNndt5uWWl8naWSqDlZw97mwmlh3bamBPJ9JkXY8pvlf/zTtK4SnwaxolPDKej7jhQzuwrr2Oi4cBRpiwGWZ503LeMfMdhKyhDS0ppXgllePh9iRrMzl04KxYhLdWx5gTGb5CCUKIozfkb4K3ve1tJJNJrrnmGny+4Vv8SYg3I6Vcduz4Fp2dyzDNKMHABEDDcVJ0tD9GqdjKrFlfxjTH1jzlivg5RCKzSKc3EvDXYhghVtvj+XluAV2eH0Mz8eWqeHbXfn6zv4v/N72e2eFDdwCqfRb/MbWeD5eq2Fe08R2Y3jLUEp7HSspxuW37fnYXijT4fb2L+nlK0VQo8fWd+xk3x8fU0LGvnKdpGlecUs+6piRd2RIVYR+6rlE5Lkw2bbO3K8sEy8fFc8cxdW4VdVOiaMP8Oq7Y3cVDK5vYuD+F6ykifpOL59TwrjMmDmnqj24YnHXNO1h+/4/pbtlHIFyGYZmUCgXsQp6qhinMu2jpEbXxmW0dfP+JbbieYkJ5ENPQ8ZSiK1viVy/txW/qvG/R8ORftKYKKBQ+c+BgJew3SRccErkS9bHD6wzvy+zjib1P8GLLixTdItNj0zl3/Lm8sP8FXtj3AgW30Lvv77f9nmumX8O7Z737mE3j67YdoG+pWp+mYWj0jGMqDw8DXXs1eHIxMXDwk8f0RUiVUnTmO4cUXCil+FVLF/c2dZDzPCK6jgJ+25rg8a4Un55cx8WVMqNCiLFiyMHFmjVrWLt2LVOnTj0W7QGgo6ODhQsX8uSTTzJ58mQA1q9fzw033MD27du58cYb+cY3vjHm5kELMVTJ1Gq6Es/h81X3CSAsqxzDCJFKr6ez6ylqay4fxVb2ZxhBZsz4PDt3/Dfp9EZ2FTzuLZ1KDosJZoFIeCqGEcLxFLsLRW7bvp8fnDyJqPnGd2wPrhEw1izvSrPndYEF9Ez7mhjwsStf4m8d3fxLQ+2ItOeCmdXsaMvw21XN7OrIErQMHM/DdhWzGsr5wpVzmF5zbEZSntzSxp2PbSVTcKgM+zB9Gpmiy69eamR9c2rIuQV102dyyY03s+GpJ2jauB67WMQfCjPnvCWctPiiQ5aI9UouTmdPJ9usDPSuGK6U4sGVjRRsl0mVr4566JpGVcRPW6rAH9fs48p544iFjv79FvT1FALwlEIf4LvJdj1MXSN4mKMW6zvW860V36It30bQCGLoBs/se4ZHdj2Cq1wmRydTE6pB07SeNWcKXTyw5QEqAhVcNvmyI74OT/WsUv76VcEBYpbZM93uNaWlw4ZOxNBJOi6e6hmp6JmQ1zM1r0CQKlqIkCLjORiagWUM7fVem8lzX1MHutZTLvcgpRTNRZvv7G5hTjjAuIDc8BRiLBhycHH55ZfzyCOPcPPNNx+L9tDR0cGVV17J7t27ex8rFotcddVVLF26lF//+td8/OMf57777uOGG244Jm0QYqR0dT6L55UGHJnQdR+aptPZsWzMBRcAAX8dc+Z8nVR6HU/uaSWfjDElYGFZr5beNHWNBr+PPYUiy7vSXFVTPrqNPgpr0jlQ9AksDtI0jYCh8UJ3ln9pOPxj5pLdZLsTWIFgz/oOBzqK2e4Erl0iFCvH8g+cG6BpGh8+bwqnT4rz902tbG/LErR0zplexYWza6iKHJsRlEzR4UdP7aRou31yC0I+k/KgxfrmJP+3Zh8fPHvykI5bOaGBxe+9nmIuS6mQJxCODHrtAMrxyK5sJb+uAy9jA6BHLIKnVBFeUMue7jw72jJUDvI6VER8NCcKrG7q5oKZh65CdTjOmFRBWcAikS31O6dSiu6czUWzaw4r6MrZOb636nt0FjppKGvo7eiX3BKt2VZc5eIop/e11zSNymAl+zL7eHjXw1zUcNGQSr8qpVjVtorH9jzGhs4NaGicUnUKl06+lFOrT+3d79zyCL/Z30X3gXUsDp67IehnUzZPydOxVImi8tD1IEUCBMgxXW0EpUgUEpxacyq1oaEF4H9rT/ZMxwr2fe00TWOc32J3vsQTXWneP65ySMcVQhwbQw4ulixZwle+8hWefPJJlixZ0ie5+4MfPPokrfe85z28973v5cUXX+x97JFHHiGZTPKtb32LUCjE7bffzs033yzBhTju2U43ujb4P0Nd81Gyu0awRUOjaTqx6KlsVDuJ+EsYukYplwXAFwyi6UZPZ1zB2nRu1IMLpRSbswW2ZAtoGswOB/uVKh3MwdWLB6PTU6HpjRQ2bmT/n//EhrWv0O6WUMEAZjRG9YyZ1E2fSfvunbTt2YXyPPyhMNPOOIu5Sy4ZMOdA0zROa4hzWsPIrcPxwo5OWlMFxpf3X9jNZ+oEfQaPbmzl3Qsn4j+MkarX84fCb5hfoVxF8tE9FDZ1olk6erjn35CXtck804ybKFCYV4nrqQGDQQBT11EoivbwrJ9SFwtw+Sl1PPByIwDxsA9d0yg5HvtTBcpDFteefngl21/Y/wIt2Rbqw/V9RhDSdhqFQtd0OvIdVAT65mOV+8tpzjTTmG5kSuzwE8f/sP0P/HLzLyk6RcJWz2v/VNNTvNTyEh86+UNcMfUKAGaHA1xeFeN3bQlKnqLCMtG1nn8bZYZB3DQp2QG6SzaWp6jRmpjGFiJeBy35NoJmkKunXT3kWQfrM3mCr1t5/SD9wLSsLdn8kI4phDh2hhxcPPjgg8yaNYvOzk5++9vf9j6uadqwBBf33HMPU6ZM4d/+7d96H1uzZg2LFi0iFOqZozlv3jw2btw46DGKxSLF4qtlC1Op1FG3S4hjwe+rRiln0BWsXa+I3183Ci0bGsd1yHd309Ldgee6aIBhWYTjFZRVVqNpPYmfo6mtaPNfu1tYlcqR93qmfgR1nQXREJ+eUveGU7Fmh4M82pHCVarfSuKe45DJZFns5si93Eng1FPRB8hJSz38MI0/+TGrvDwZU8enNIzuFF4my+5kgq0vPkswUkZZVTW6YVDMZVnz6MO07d7JxTf8E77g6JeSbUv3fLYOlggd9hmk8jbpgoM/cmyqU5X2JCls6UIPW+ivWXHciBl4BYf85i7ik8oI+UyyRWfABOpcycFn6oed/3A4rj+np0P/yLoW9nbl0Oj5bpwYD/IvS6Zz8rjDK0ywJ7UHT3n9Rh8OFlExNZOsne1d9+IgXdNxPY/dHTn2d3QQtAzm1EcPmUC+pWsLv9nyGwzNoCH66rBbZbCS9lw792+6n5MqT2JKrGch3X+dVEvcMvi/9iT7iiUUEDEM3lYT558mVpFxSvx08+9Y0/I0bqmFpKaRBCoCFXxgzgeOqOqVT9fwDvH54SmwZJq0EGPGkIOLJ5988li0o9eUKf3vtqRSqT6Pa5qGYRgkEgni8f537O644w6+9KUvHdN2CjEcKirOo7XtLzhOCsvq2/Fw3Z47cVVVF45G0w6bXShQtm0jyUAMv1KYlg9QuI5DsrUV27bxYlVvmNB9LGUdl1u3N7MmnafGZ1Lr6/noS7seTyUypJx9fHP2BMKDrLUAcFFlGb9p6aS5UGJiwNcbDNrt7bQkuvHbNmf87Q/s72jFN3488Q9+gMi55/Y+v7h9O5333sduZZP1+4joPRW0UOAWC9i5LMowsUsl/MEwmq7hCwRx7BL7t21h83NPM+/iI0tsHk5hv4Fi8NyCktszWnC0FZEOpbAlAZ7qE1gcpAdMvKxNYHeaC2ZW8fvVzUSDFtZrgiHvQJWrueNjnDxu+BKBfabOTYuncc388azckyBfcqmLBVgwKT6k12OgfAeAoBnE0Axc5WJqZm9FpoPaktCxfwnf3NNB0W7H1DXqy4O8Y8EErjilfsAbGMsal5G1szSU9Z/PVxWsYk96D081PdU7EmLpGtdPqOYddRVsyORxlGJy0M/4A/kONX4fX1rwQRpTF7KidQU5J0d1sJqz6s8i5j+yqm+LYmE2ZQoDvudsr2dE8fTokVUTE0IMvyOqwffcc8/x2c9+lve9733s2bOHf//3f6dQKLzxE4+QaZr4X1f/PBAIkMvlBtz/c5/7HMlksvensbHxmLVNiKNRVjaX6qrLsJ1uCsUWXLeI55UoltopFluIl59JRfy80W7mIe1avYKJm1YSQpENRkDT0DQd0/KhmQbNRYe4criocmTKtA5keSLN+kyeiQGLMtNA0zQ0TSNqGkwIWKzP5HgmkTnkMap9Fp+eXEfMMtmVL7KvUKK5o4ud3WmwHd67fSNzAxZmdTWl5mba7/w22Zde6n1++sllFNJp2v0mlqa92knSoGRZeEph6BquXaKQTfc+z7R8mJbF9pefx3OHZwrP0Vg4uYJIwCSRs/tt85Qimbc5e1rVEZWlPVxusgiHKiFr6LipIu9dNIk5dVGaEnlaUwXSBZvWdIF1rWm6TcW2OouPbtrDb1u6yDjD99rWRgNcfko9b18wgXOnVw050Dqp8iRM3aTg9P1eDVkhwlaYkluizFfWJ1hIpHV2bF9AMdNA2GfRUBGiusxPS7LA95/Yzu9XNQ94rp3JnfgM34CBh6Zp+HQfO7p39NsWMQ3OKo9wbrysN7B4rYnRiVw741reN+d9XDb5siMOLACWVpdT4zdpLJR6piceYHuKvYUiU4N+LqgYvc8XIURfQw4uvvvd73LZZZexZ88efv/731MqlXjppZeOWYI3QEVFBe3t7X0eS6fTg5bC9fv9RKPRPj9CjEWapjF58r8wqeFGfFYFJbuDYqkNwwgybtx7mD79sxjGsS9terhsO0Wp1IHnOb2P7XjlZcanOri4bRcAbYEw3VaAhC9AZzhGoFTgH7r2jmoFqKcSPZ11n97/I89/oKzl013pftte79x4Gd+d08AHxlXR4LeobG7i0s1r+cKa53lrc8/1634/1vjxeLkc3Q880DuVpbh5E3bAh6sU5uunVR2orqN5Pfu6ttNnuxUIkE+nKBVGf175uPIgl8+tJ1Nw6MgUcQ+0uWC77O3KUV3m59rTju0CkFrYAtcbfAfXQw9bVEX8fOXaU3j/ogaiQYuM7dJccijWBQjOq0SVWWzOFrhzTyv/sbWRzpIz+DFH0Pya+cyMz6Ql10LJLfU+rpQiZIYImkE85bE/u5+uQhfN6Wa2N1VguNWcVFdDNGChaxp+02B8eRBD1/jVS3vpzpX6ncvSrd4KUQNxlYvPGN0qTBMDPv6/qeOo9VvsLZTYmSuyM1dgX7HE9FCA/5w+DkvTaC6UxszfUIg3syHfWvr617/Oo48+yjnnnEM8Hsfv93PvvfeyYMECfvzjHx+LNrJw4ULuueee3t937dpFsVikomLsLS4mhi6T2UJ7++OkMxvRNIPy8jOoqryYYPDwkh+Pd7puMW7cu6itvZpcbidKuYRCkzHNsXMnrju5kpaW/yOdXo9SHn5/NTXVS6mpuZJsogvL5+eszibG5dOsKa9jbziGoRTT0p1M2b6OabNmj2r7k7Z7yDnZptazuvDhmBz0888NNXywo5n9D/wEs6oK/XUjq5qmYVZWUtyxk9LOnfinTQPDxHR75sh7PbHEa/bv+e/BQER/3fQs13EwLevAlLPR9+HzpmDoGg+v209jomcE2dA1ptVEuHnJ9GNWAvegwIw4xW0JlO2ivW5UQNkuaBqBGT1TZivCPj583lTet2gSt29p5m+JNDMj/j6BZsnzWJXKcU9TG5+demwDo8Nh6RafXPBJ/mvFf7EtsQ1Xuej0JKCX+8v5yLyPUHJLLG9aTrKYpC46mZSzkJryMnxG/6/16oifxkSOF3Z28Za5fXO4FtYtZE37GlzPxdD7vpaO15MPtqB2wTG93sNxRizM3SdPZnlXmq3ZAoYGc8tCzIsE+Ut7N3/rTJG0XUxd47RoiHfUVnBadPRzlIR4MxpycGFZFsbrvviy2SxlZcfuy2Tx4sWkUinuvfdebrjhBm6//XYuueSSfu0Qx5/W1j+zd++PsZ00hhEApchkNtLW9gjTpn6a8vIzevdVSlEoNB/ITygnEBj9TsCRUMollVpLNrsDTdOIROYQiczBMAKUlZ002s3rp739MXbtvgvXzWKZ5ei6RSHfzO49PySV3kAoFiHT1QnAxFySiblk73OVUnRnkgRHefSwIehjXWbwu/4lTzFhiDXyvWwWHBttkBFUzedDOU7PfkDojAUU1q2jwgjR4hSxlNY7FcWHjqYUrgaWaRGIvFqaWCmPUi7LtPMuxBzGhUtzJYdVe7tJFxyqy3zMm1DeJy/hUCxD58bzp3LN/PGs2pug4HjUxwLMn3j4xzgagWnl5BuilHYn0UMWWrDnq0zlHbycjW9yDP/U8j7PacoV+Vtniu6cTTJRwDI0qiN+qsv8+AyduGXyVCLDDUWb2kFWkh9JdeE6vnreV3m55WXWtq+l5JZoiDZw7rhzqQ33lHJ9+8y3966S/pENKwj5Bv5ONPSe91oq338q2/kTzueRXY/QlGliXHhc7xoUJbfE/ux+GsoaOGfcOcfuQocgbpm8rfbVPMuU4/L5rU2sSuUIHVhvw1aKZZ1pVqVyfG5KPefLdCkhRtyQg4ubb76Zyy+/nJtuugnbtnnwwQf59a9/zcc+9rFj0T6gJ+fiRz/6Eddddx233HILuq6zbNmyY3Y+MTLS6U3s2ftjUC6h4OTejpZSHvlCMzt33sncud/F56sknd5Ac/OvSKXX4Xk2uu4nFp3P+PHXEYnMHOUrOXz5/F527PwWmcw2lDpQm1/3ES2bx9Spn8TvP/p6+8OpVOpi794foTybYGDSq+tXmGW4bo6urmepn3cprTs8XNvGsPp2ykr5HKbPR8PcUwc6/CF5hQKFjZtQxQJmTQ2+qVOPeOHMSypjPNaRImk7xKy+H3vdtkPA0LhkiCv8mlWVaP4AKp9HC/W/Q1oollg1bRbdegirqZ05C8+h5uFHmNjZRneZn6znEtB1TKVBoYhh6dho+EPh3ut0SkXSXV1E4pXMPnfxEV376yml+NPa/fzmpb20pYt4qicBu6EyzI3nTeGsqYe/VkB1mZ/LTh75amaapVN++RTSTzVR2NGNd2ARPS1oEJxbRdkFE9GsV4OcRLbEfz62hX1BD3/Jw9A1CiWPPZ05OjIlZtVGiJkGjYUSO/PFMRFcAPgNP+eNP4/zxg+ed6VpGmUBk4ClU7DdAXNdHM9DKUX5AIsFVgQq+PQZn+Y7r3yHxnQjnvJQKAzNYEpsCp9c8Mmjypc4ln7XmmBVKsf4gIX/NSNR5Qf+lt/f28bpsdAhCzUIIYbfkIOLW265hdraWu6//34aGhr429/+xr/927/x/ve/f1gbpl5Xdu7qq69mx44drFy5kkWLFlFZKYvlHO/a2x/DddIEXxNYQM/aCcHAOPKFvXR2PkU4PI2t275KqdSJ31eJZZbjunm6up4mk93CrJm3EonMGsUrOTy23c3WbV8ll92B31+LYYRQSuG6WRLdL7Jt++3Mmf21MZVj0dX1DCW7i2BgYr+OvWGE0DQdI7ab2mnTaNm+jUCkDH84AkodyBEoMOPMs6mdOv2wz6mUIvXwwyR/93vstjZwHPRgEP9JJ1H54Q/jn3r49fsPWhANcVV1Ob9vS5B2PcoPrL/w/7P33mGSXdW99rtPqhw758k5KY2kURhJoCyBlQ0CAUZgY8DGhHuNr4nXJhjbH8YGY8ElGDDBgBEIlAXKOU5OPZ1z5Vwn7O+PmulRq7tH0z3dk1Tv8+iBqVO1zz7Vdar22mut3y9xwFX4+obwjEsoXMuX41q8mML27Rjt7RPen73+EF8/7XwGWtpQsibkxtCFYPn7/5I/+dF3WN21lz0uQVaBAiBcGjXtC/A3NZEeHSE5MowQoCgqNS1tnHP9LUSa5qZM8DevDPKN3+9FAE0hN7paWZTuH83x5Xt28ulrV7OmIYDjSHSXOuuAbr5RvDqhKxbiS5awhivZIa3BhxaefP/855Nd7BvJoi/04dLVSrOhWvms5UoW3fE8C+p8KEJM6oc5GfC5NC5cVscvnu8n4jVQX+PtMZopU+t3TRs4Lo0s5Subv8KzQ8+yN7kXgWBpZClnNZ6F6wT6Pno1Zcfh7tEkHlWZEFhAJeBqchkMlMo8mczNeOOgSpUqR8es5Dxuu+22OfG0mCmNjY1cffXVx/y8VeaHdGbLgQXqVColKqCQyewkFn8EsxyfkN1QFANNC5AvdNPX9wOWL/+/J+wi6CBjY78nn9+H29M2bpwnhEDT/CiKTiazjUTySWprLjom87HtEtnsDhynjNvdhMfTNuk5pdLggXlWFuMHg/6D77Wq+iibw1z4js/y4u/uomfbK6RHhkEIPIEgK8+/iPWXXT2jv03qV3cS/+53QChodXUITcPJ58k/9xzmQD9Nn/scRtvkuR4ORQg+3NHAAq+LO0cSDBQrWaOFHhdvrQ9zbX14xp8foShE3/1uhr/0JczubtRIBOFyMSYUvnrWaQzV1NFWE8HjrSzO8rbDK46Lb73vQ/zdwD5ad+0gns1iN9Th37CBlnUbMNweMvExBvfswi6XCdTV07x0xaQejNmSL1v89NkeBJXG7IO4dZX2qIe9Qxm++pMtXOO4AUGozsPi0+tZsK4WZRozuuONFnZNGVAcJJYt8fDuMeoQZBwoq+A+0F4jhEDXFFJ5k+GCSa1bZ6VvelfwE5kbTm/l+a4E3fE8NT4Dn6FiOpKxbAlVEbz97HZCnukzMh7Nw4WtF3Jh69xkyOabpGmTsmz805Th6Qc+r8OlyaVgVapUmV9mHFzs2bOHv//7v6e3t3dSduGhhx6as4lVOfURQiA5nLGaxLIS5HJ7MFx1kxZ/Qii4jFrSmS0UCj14vR3zO+GjJJ54HCH0KR25FcUFSBKJp+c9uJBSMjz8GwaHfkmpNIyUNqrqJRQ6nfa2905opFcUN0iw7CLl8iimGUNKG0VxHTAAlOh6EF8oygVvfzeZ2BiJwQEURaG2fcGE3oEjwUokSP78v0HT0evrxx9X/X4Ur5dyTzepX/+aulmo02mK4LqGCNfWhRkoVVRzml3GtA7OR4Jn7RoaP/0pkj/7bwqvvIyTz/PYho0MNbWyMBJE9x3S3veqCm1unZ0lk1c2buLSq65kqoKiQLSWwNm1s57T4XipJ8lIukRTaPICOp8xUbIWux2TeECnRtMY7c0y1pdlrD/LmVcuOGEDjMPRlyiQLZk0Bt005R26AiqmItGcSk+9qghyUpKxbN5dV4v/CFzFy70ZCttjlPszIASuhSE8q2rQ649fA3Fz2MPn37qG7zy+nxd6EqRSJqoi6Ij6uOnMVi5d1XDc5jYfeFQFVQjK05jrOVLiAO6T8DNbpcrJzoyDixtuuIG6ujr++I//eFop2CpVjoRQ6AxyuZ9N6U7tyIqcoMvVSCr9Iqoy9Y+2ongwrSSmGQNO7ODCtrJTBhYHEWjYdm7e59E/8BP6+r6PQMVl1CGEhmVlicUeoVDoZeWKv8flqizsQ+Ez6Ov/IdnsNhynfCCDIbCtHDkrixAqdfXvRRww/QrU1BKomf3COP/cc9jxBHpr66RjQlFQA0Fyjz9O9F3vRvXPzjRLUwTtnrkr9XAvX07jp/4Wc3gYJ51ma8bBa0n0KRrEjQOyt0+nclxae+zr2DMlC0fKSU3XlumQHs2jSShrAjwabkPH7dMp5S06XxihaWGItlUnn0KfVMDRBKYjactKSorDsFdQ1gXIyvaGBC7werm1+fU/u7nnh8k+0Y8sOweUqiT554Ypbo8RvLRjXKXqeNBe4+Wzb1lNbzzPcLqIW1dZ3hg4Jk32x5qApnJOyM/dYymi2uTyvYRlE1ArXhxVqlQ5tsw4uNi/fz933XUX7e2T3TyrVJkJdbWXMjp6P8VSP25X8/gCVUqbYrEPt7uVSPRcRscewHFKFTWp1+DIEkLoqCeQbOt0eLwLyeU7pzwmpURKE497fu+rUmmEwcGfoyhuXMah5nFdD6FpfvL5ToaGf0NH+3sB8PtWHugLyaMoHhTlYFmFg2UVgMN4DcwCJ52umPBNUwYkXC5ksYCTy846uJgv9IYGaGig9Eonmj19KYYCFA/n0TCP1PoNNKXSY/FqY7dCtoxtSfIqFByHp7JZanWdlW4X9V6dYs6k8+XRkyq4GCmZ/M9wgnvHUvQu9dNlOrSWBW05h8YCxNwKJQXKBYv6kuRzl7SOl9JMR7k/S/aJAUCg1RwqK5NSYidLpB/sQW/wogaPb59CW9RLW/TUl2G9oTHCM6ks3cUyTa5KU7cjJQnTJm3b3NQQnbEKXJUqVY6eGW9nvOtd7+KOO+6Yj7lUeYPh8y1i0cK/RNMCFIrdFAq95AvdFIq9uN0tLFnyvwmHzsTjaaNcHp30eikl5fIYPu9ifN7Fx+EKZkZtzcUIoWOa6UnHTDOOqvmoqZl5vXO5PMbA4M/ZsfP/sH3H/6an93vk891TPjeReArLSmPokxs7hVDRVD+xsd/jOJWyoWx2G4qio+s1gMS2C9h2Accpo2l+XK4mUsnnsO3SjOc9FWo4DIC0pjbCksUiwuVGnWG51bFkqddNYZrgQUqJLSu9HseD9a1h2mu8DKdLE8payyWbEWkxbFmUHMnWUok/ZLN8Nxbn95kMqqGQGMoflznPhoFimU/s6uU/B2JkbIdar4EloMsn2BJV0CQsSNs0DhYJDha4aVkDIc/rL0IL28dwSjaKf2LvghACNezCyZoUdyXm67JOSF5bHn0sWeX38KnFzXS4XQyXTLoKJbqLle+uWxqj/Fn7iaW+V6XKG4UZZy6i0Sj/8R//wb333svmzZsnuF9/+tOfntPJVTm5cJwyyeSzxBNPYllp3K5mamouxO9fOW2zbE3NBfh8S4jF/kA2uwuESii4npqaC9H1MAAtLW+js/P/o1Dsw2XUoSgubLtE2RxBU320tLx9POtxIhMOn0lD/VUMj/wGy06ja0GkBNNKIoRKS8vb8fmWzmjMdHoLe/f9A6XiIEJoIBSSyecYHv4NCzo+QF3dmyc837RSgECIiiGXLNlIWyIUgXCrKKob28kfyFQY5AvdSGkTCKzEtgtYVgopJarqRtfDOHaRspmgXB7G4zn6rIv3rLPQamqwxsbQGyd2JEjbxs5mCb/5zSi+Eytr8Wouqw3y+3iahGkReY3s7UjZIqipvKn2+KjXaKrC+y9cxBd/t4OuWJ4av4GhKvTmS6SQuISgRa/sAEspyToOj2dzGKrDptCJqRo0Fd/qG2VPvkiHx4UmBNLQ0R0YTBdJ6YIXdZvmoSJeQ+Ut65t5+9lHVlJpDuRQDGUaEQoBAszh+S9tPN7kbJv7xtLcPZpkqGQR0FQuqw1yVV2IOuPYyvhuDPv5dsDLs6kcAyUTtyrYGPLR5KpmLKpUOV7MOLjo7e3lqquuAiAWixGLVcyzTnSlnirzi2mm2Lvvy6RSLyCljUBFYjEy8jsaGt5Ce/ufTBsAuN1NtLS8bdqxa2suAinp6/8hxWJ/ZXyh4fG009b6LiKRjfN0VXOLEAoLFvwZXt8iRkZ+R6HQhwCCwbU01F9NTc1FM7qPTDPJ3n3/SKk0hMfTPkHRqVQapKv7G3g8bRNkenUtBEjsQhk7Ua4EFwd6XoShYAezGL4IqlopqVCEDkiQEk31oqkTSy0qfwtRCWzmADUYJPK2P2bsjm9R7utDq6lB6DpOLocVj+Na0EHorW+Zk3PNFxtDPm5sjPLfQ3HSVomgpiKBlGXjURRub61lwSwzF1JKsG2ENvv3+6wFUT5z7Wp+8mwPW/srpnJ5x8ELNGmH/AKEEARUlbhl8aJZ5IZlC2Z9zmPJQLHMU8kcUV0bl5UVolIqVON30Z8tUnTpXNNax1uW1rO4zn/k950iOKwGBYBy4m90HA0Zy+bTe/p5Np1DEwKvojBUKnNH7ygPxtL83dKWOe1pOhLcqlI1y6tS5QRixr9Q3/3ud+djHlVOcrq6vk4i8QxuVyOqWqlFllJiWkkGh36O291EQ8PsZYRray8mGt1EOr0F00qh62GCgXWv6gGYGVJKilu3kXvqKex4HLUmin/TJlwrp8+yzAVCqDTUX0l93eWYZhwQ6Hp0VueMxR+lVBo44EFxqH5eCIHL1US+0MXo6AMTgotI5Bx6Or9HITGAVg4jVAWhKCAldqmMmUlR570KRans+gUCa9FUP5aVQtcnN6qWzQTBwBpcrqaZvxnTELjySoTbQ/KXv8Ts7UXaNorHjf+izUTfeRt609ydaz4QQvCBtjqW+dz8ZiTJ3nwRAVwY8fOW+gjnzKLBtNzXR/qee8k99hiyVEJraiJ42aX4L7kEZRbCGuvbwqxrDTGQKvL7ncN865H9BIoOZsFGikomCyr3iduGtCrRFpy42aJX018yydk2LVPU2nsNlcURL93FMhuX1bMkMrMFqWthkNxwfkoRCmlXog6j7cQt2ZsLfjQQ45lUjmaXjvtVjeKWlOzLF/lq9zD/tHyyL06VKlXeOMxq++trX/sa3/zmN+np6aGjo4MPfOADfOhDH5rruVU5Scjnu0kkn8HQo+OBBVQWWYYeoWDnGB6+i7q6y1GU2e+4KoqLcPjMo56vUy4z9vWvk334EWSxCIqCdBzSv/0dgUsuofbP/hShz29qXwgFwzg6udFsZgdIJgQWh8YXaKqXVPrFCY8bRh3h2EWMiF9iuuPoTgiBiq3msfQ0RqEZ7471yPUSoQo8nhaiNRcyMvJbhNBQ1cour5QO5fIYiqLT2PiWOV1ICCEIXHIx/gsvoLRvH7JQQGtomNegQjoO5sAg2BZaYyOK6+h2XoWoOH6/KRogbzsIIfDOUrGnuGMHw1/+MubQMKrXC7pOafduRnftIv/889R//OOzmq8Qgpawh8agB1UR1DT5SA4XKBVMDoi1IQDdUFH8Gob/xHCtfj1cikAVAsuRaOrkz6UlJaoQk4zXjgTPqloKW2PYiSJq2H0oCLMd7GQJLeo+rmpR803Gsrk3lsKvKhMCCwBNCOoMnS2ZAjtzRVb6PdOMUqVKlVOdGa/0Pve5z/GjH/2Iz372syxevJh9+/bx2c9+lkQiwac+9an5mGOVE5xsdie2ncNwT71Y1rUIxWJ/ZZd9Duryj5bEj39C5r77UaNRlIaGA4tliZPJkL77brT6OiK33HK8p3kEyMrqbwZYsSLB/edBjUoy/DCmOobEQZFugsUzqU1eg0h5MAeyGG2VXd0FHX+KbRdIJJ6gVB4bH0vXQ7S1vJtodH5Mt4Sm4V4+v87rUkqyDz1E6jd3VbIkjoMaiRC87FJCf/RHKO6jM1QTQuA7At+EaedXLjP6b/+GNTJScQE/uCCORHDyeXKPP0F65e8IX3/drM+xoNaH11DJ2w41rX7KeYtcziRvWii6gqUKanwGrZGTY7G4wuemxW3QXyzTqk7OXoyZFo2GxupZLH61qJvQFQtI3deNnShWHpSAAK3GTeiqRSju2W2g2DkTa6wAAvR676zHmU/6imVSpk2NPvXc/KrCqGnRXSgfVXAxkh/h6cGniRfjBIwAZzWeRVtgZsaZVapUOX7M+Nvrjjvu4Le//S0bNmwA4Oyzz2blypVcc8011eDiDYp8HTnSg2Z5UtrHaEbTY6fTZB+4H8XrRQ0cKokQQqAGgzjFIum77yZ0zTUndNMwgN+/ktGxBw70PUxcwEopsZ08weCGiY8XLLAhUryQ8OgmikY3jihjWLUYdgMSiW0VcQqH1JpU1cvSJZ8km91OIvkMjl3AMOqIRs/H7T6xS5Rej+TPfkbiR/9VCSrCYRRFwU4kiH//Pynt2Uv9//5fsyo7mivyL7xIubsHraHxUGBxAMXrReg6mfvuI/SWa2fdh7G4zse6tjBP7ovhVhWGCyVG8iVMWyILFXWrGr9B2To+8rkzxVAUbm6M8s9dQwyXTOoMDUUIHCkZMy0sR3JjY3TWmSTXghA1t66ktDtRad5WBEazH9fSMIpr5n8Dp2SRfXKQ4s74+H2neHU8a2rwb2xC6CdOD4euCBQB9jSNJw4gJLM2ppRS8qu9v+Lnu39OupxGUPnt+Nmun3Fpx6W8a/W70I4i+12lSpVjw4y/tQzDIJPJTHgsk8mgz3MZSZUTF593EYriwbazUx43zRQuox63u/kYz2wypd27seIJ1MjUpQtqOIwVi1Pat+8Yz2zm1NRciMvVSKHYh5SHFn5SSkrlITQ1QH3dpRNeIzwaQhVI00HBwFteir+0GsOuuPdK00FoCopn4g+4EIJAYDXtbe9hwYI/p7n5pmMWWEjbxk4mcfJzK4Va7ukh+fNfIAwDo7V13AVcb2hAq6sj99RT5B55ZE7POVPM/j5wnGnLnpRAAGtsDDsxe/lTIQQfungJS+t9bBlI0RXPVwKJAxKjAbfGQLLI536znUxxev+OmZAumtz1ygD/cM9OvnT3Dn71Yj/xXHnS86TlUNyXJPvkANmnBih1p5HO60ufXlsX4v1tdRiKoKdYHpco1YTgPS213NBwdKVLqk/He1o9oSsWErpsAZ41tbMKLKTlkPrdfvLPDSMtBzVooAYMZNkm99Qgqfu7j+h6jxUL3C7a3S7i5tQbRQnTJqyrrA/MLmvxYM+D/GD7D7ClTVugjfZgO+2BdlSh8ut9v+bnu39+NNOvUuWU4c4772TRokVomsaGDRvYsWMHAH/xF39xQGSl8t+SJUuOy/xm/G348Y9/nJtuuomPfOQjLFq0iM7OTr761a9WZWjfwPh8ywgF1xFPPIWiuCc0WVtWFkeWqa+/AkU5/lKW0nY40Kgw5fGDzc3SPv5ZltdD18MsWfwJ9u77BwqFboSiI1BwnBKaHqKj4/0TmrmhUrqhN/spdaURromutlJKnGwZrc6L3nz8m1KdUonMPfeQvu9+rNFRhKLgOeN0QldfjXvVqqMeP/f44ziZDHrHZBlSxeuFeJz4Aw+TaD4Nq+TgC7uo6wigzHJXdjZUshFyygZiAGwbFOWoe4Sawx5uO3cBW/oPerAIXLpCnd9Frd+F7Ui2DaR4aOcIb93QclTn2j6Q5sv37KQvcShYvG/7MD97rpePXbacMzoqC39zJE/q3q5KqdDBBbYq0Jv8hK5YgHYYaVwhBG9vquHSmiCPJ7LETYuQpnJ+JECD68TZCCvuTVLan0YJGijGoeyjGjBwihbF3XE8q2twdRwf6eLXoimCGxojfGX/EKNlkxq9khWSUpK2HTKWzS1N0VnJ0VqOxa/3/RqJpNZzqMRWCEHEHcFyLO7tuperF11NwKgqQ1V547Jv3z7e85738M1vfpPNmzfz4Q9/mNtvv53HH3+c5557jt/+9rds2rQJAHUaQ9r5ZsbBxQc/+EEaGxv51re+xQ9+8APa29v5xje+wfXXXz8f86tyEiCEYMHCD2FaKbLZnQihoggD2ykihEpt7ZtpbHzr8Z4mAMaCDhSfHzudRpsie2GnUqgBP8YUC87ZIB1JqTNJYXsMaziP0BVcS8J4VteiRY6unh8gGFzH6lX/zFjs96SSzyOljT+wirraS/B6F056vhAC/7nNWGMFrFgRNaAjdBVpOTiZMsKt4t/UjJiiEfZY4pRKjPzjP5J74kmEqqL4/UjLIvPQQ+Sfe576v/wLfAe+PGeLOTxScQOfYtEugb7QaQzEViB/tgcpQdUE4UYfZ1zeQV37sVncuNeuQ/F4cTIZ1ODEBaaUEjuVxHv22Sih0FGfa+tAGreusrIxMOk9URWBqoijDi4SuTJfunsH/ckCrWEP2oHSJNuR9CUKfOXenfzzzRto0FSSv+3EjhdRg67x0iCnbGP2Zkj9tpPIjcsmLMinos7Q+aOjzFLMJ8WdcZByyutQ3BpOzqS4O37CBBcAV9WGiJUtfjwYp6twKNvkURWuqgvxvtbZGdftT+1nIDtA1D21C3zEHWEwN8i22DbOaTpnVueoUuVUYMeOHXzpS1/i5ptvBuADH/gAV199NZZlsW3bNi688EL8x9lodlbFizfccAM33HDDXM+lykmM29XIyhVfYCz2B+KxRzGtFG53C3W1byIcPvuoVKLmEr2hAd+555K5914cn29CPb1TKmFnMoTe8ha06NQ/cDNBOpLMw33kXx4BWyIMFfIWuaeGKG6PE7pqIUbr0S9SXa56WppvoaX5yJrQjRY/4WsWkX1iAHMoh5MzEaqC3uTHf04TrkVHv1A9WjL33U/u8SfQamsrWYQDqJEI5sAAY3fcgXvt2gl9MzNFDfjHS39eSxeL6HIvQFUVQlE3iiowyzaxviyP/fceNr99OdGm+e/JMRYuwHv22WQfeggUBcXnq/Qw2TbW6CiKx0vommvmRK0rkSujiuk9i9yaSiw7uXRpJjy8e5S+ZIH2iBf1VRkgVRG0RT10x/I8uHOE61UXdqyIGj2kyASgGCoibFDuyZD4xR4Ur4YaMHAvjaC3zsCv4gTBzpQR2mGqkxWBnZmbUrSDDGQHeKz/MfoyfbhUFxvqN3Bm45m41CPLLAshuK2llouiAR5JZBktmwQ0lU1hPyt97ln/DUzHxJEO6hTqdwCqUHGkg2nP7ftRpcqJRDqdnvBvl8uF6zVlsddcc82Ef+/atYulS5eyZcsWHMdhw4YN9Pf3s3nzZu644w7a24+9kM6sVnx33nknd955J8PDwzQ2NnLjjTdy5ZVXzvXcqpxkaFqAxoZraWy49nhP5bBE3/0uzKFBilu2VhZsLhdOqQSOg/e004i+8x2vO8a0ZSqvorgrTv6lERS3iuI5VCYgpcSOF0nd303NrStfd/d1PjBaA0RuWoY1WqgEF24NvcE7YSF3vJBSkrn//krGwjvRtE8Igd7QgDkwQO6ppwheeuk0o7w+3jPPJPXrX2Pncqivat4v4aLPaUdxyvhqa1AOZHF0QyVU5yY5UmDXU0Oce93iWZ/7SBFCUPvnH0CaZfLPPod9wLQUKoFW9D3vxnvGGXNyrlq/gS2n/2wXTJslDUe3G/ZibxIFMSGwOIgiBIam8FxXnKvwgComfR6lI7HiJexMmeLuOGrAhXQkhS1juFdFCV7cfvjF+gmGGjCwRgvTP8GRqL65K+O6p+sefrDtB6TKqfHF+v3d97M8upyPn/lx6rxHnnVo97h4xxya5TX5mvDpPrJmlqg6eXMnZ+bwaB5a/EdXllelyolMW9tEVbTPfOYzfPazn532+eVymX/6p3/iox/9KNu3b2f58uX867/+K7W1tfzVX/0V73//+7nnnnvmedaTmZUU7b/9279x6623snLlSnp7e3nnO9/JRz/6Uf7mb/5mPuZYpcqcokUiNH7q0+QefYTMH/6APRbDVV+Hf/NF+C+8YNKC9iBOyaK4PU5heww7U0bxaHhW1uBeXTNpASClpLB1DBw5IbCAA8pUYRd2okipM4VnxdFnSWaDEAK9fuprPZ44uTzm8DDKNGndg6pI1uDgUZ3HvWYN3o0byT76GJgmSjAIQjBWClBWFXwuiVYzUV5ZCIHbp9O/O0Epb+Lyzn/9vur30/A3f0Nx+3YKz7+AUypWMnDnnYdWUzNn5zl/aR2/emmAVMEk7J2okFU0baSEN69sOKpzWLbD4eJXRYBlS6TtTFmaZyeKONkyKALFpaFF3UgpkUWbwitjqEEX/rMnCw30xvMMp4u4dZXljQH0WSpFzTXuZRFKnUmkaSP0iZsMTskGReBeNjdlXS+NvMR3t3wXB4eOQMd4AFmyS2yLbeNfXvgXPrfpc6jK9JsdUkqG88Nkyhmi7ig1nrn7/EXcETY1b+K3+39LQA+gq4fuLduxGSuOcWbDmSwMTS73rFLlVKG3t5fgq0pgX5u1eC2f+cxn8Pl83H777ei6zq233jp+7Bvf+AYLFy4knU5PGPNYMOPg4t///d+58847x5tFAG6++WZuuOGGanBR5aRB9fsIXnklwSPMuDl5k+RdnZR7M6AIhK5gJUpkHumjsCtO+C2LxxtMpWWRfexJ8i8kcYpFzH4LNRJBq6sb900QqgKSSrNqlQkoho7QNGR56hIcKSVIiTCO0uhOVan7y4+g+HzkHn8Cs68PgHIogoi4cC9qR5miMVXVBGbJoVy0j0lwAZWgxrN6NZ7Vq+ftHCsaA1yxppE7X+ynaDpEfDqKEKQKJumixVkLIly0fHb19OPnaAry5L4YjpQor3W4lpKC6bCqOYASk5R7yxXtBUNBcWtIR2Jny0hRkTvFqAQIQgiER0OalQDDe1r9eDawJ5bnO4/v54XuBAXTRlMErVEvN5/ZxptX1h/3Mir30giFHbFKU7dHq6i0ycr3jSzZuFdEMNrnZlFw9/67KVgF2gIT3bNdqosGbwM74jvYGtvK+rr1U75+W2wbv9j9C7bHtmM6Ji7VxVmNZ3HjshvnzIPilhW30JnqZGd8Jy7VhUfzULJL5K087YF23rv2vcf9b1alynwSDAaPOBB46KGH+PrXv85TTz01pWJrfX09juMwODh44gcXkUhk0kVomobvBPcEqFLlaMg+NUi5J11x5X1V2YW0HazhPJk/9BJ56xKkZTH2jW+QfuAh9I5bQNWQZhlzYAA7FsNYvPg1/hqV/7XTZQrbYxR3x5FlB63Wg2dlFNeSyHFvrj7WCMPAd/ZG0r+7GxmNTlpMONkswu3Gc9ppR30u1e+j/iMfwbzpJgrbtoFt48hG+p4pIafxtzBLNrpLwz2H5SonAkIIPrB5MQ0BN79+ZYDRTAkpJUGPzo1ntPDOcxfgNY6ud+qSFfX86sV+BlNFmkOH6vOllAxnSgQNlbOHTUq9OZy8hVOwEIpAuFQUv1HpXRICVFD9E/8+ilfHzpSxRvIYrQH6Enk+fedWuuN5anwGEZ+OaUu6Yzn++f7dFE2ba9cfX3lsoSuEr15E5rF+SrsTFWM+ccDnYn0dgXOb5uT+L1gFtsW2ETAmN+sDeDQPI/kRdsR2TBlcvDTyEv/03D+RLCWJuCMEjAAFs8AD3Q+wM76Tvz3nb48qwLAci+2x7Qzlhrik/RLW1q7lqcGnSJfShFwhrl18LZcvuJx6b/2sz1GlyqnE/v37edvb3sbXv/51Vh1QT/zEJz7Baaedxtvf/nYAnnzySRRFmVRqdSyY8S/FBRdcwLXXXsv73/9+2tra6Orq4tvf/jY333wz//mf/wnAbbfdNucTrVLlSCiX4xSL/SiKgde7aIIs7mxx8ibF3QmEW5tUzy1UBcWnU+7JYI4VKDz/KOn77kcNhRBqGvQWUG2ElDjFIuWu/bhXr0FasiKr2RrAHMmTvKsTK15EaApCFZT2JSntT+FZlSb45vZKpuMNRPCqq8g99TRmXx96YyNC18dd1O14HP/mzbiWLZ2z8+ktLegtlVpud9Fi++4t5JJlAlHXhMWYY1cyFkvOaEB3HR+Jv/lEUxVuPquNa9c3s3ckiyMlHTXeSWVSs6Ul7OFDlyzhXx/cw/5YHq+uIgTkyzZ+l8rb3F5a+/IIvw4CnLRZKXvKW5UyIVsiNNAinul7lQ706P/y+T664zk6or7xHg9NgbaIl8FUgR8+1c3m5XUE3cc3SFTcGqE3d2Cf3VTpvxCgN3hR5jArVsn2Td+sDyAQ2FMYnVqOxX9u/09S5RTtgfbxMVyqi6ArSE+mh5/t+hkfO/Njs5rb9th2vr3l23Slu7AdG4kk7Apz+YLLuXbRtfh032FLtY6U/an9PNr3KHuTezFUgzMazuC8lvMIGke2oyulZKBkUnQc6g2dgHbq3f9VTg4KhQLXXHMNb33rW7nuuuvIZiseY+vWreNv//ZvaWhowLZtPvzhD3PbbbfhnabUez6ZcXCxZ88eVq5cyaOPPjr+2KpVq9i6dStbt26tKElUg4sqx5hyOU5f3w+Jxx/FsrMIoeJ2t9DUeB11dZcfVSrdSpRwChZqYOoFlnCrODETa6xA5r57ASrZCbMHtAYQXiBfaRwvFLFiSYRwY7QH0Zt9JH66CztRRHuNMo5TtChsi6E3evGun7hj5xQsijvjFHcncPImasSNe2UU9+Lw6za0WlaWsdjvGRv7PWYphuGqo7buTdTWbEZVT4weDNeSJdR/7KOM/fu/Yw4OVVSdpETxePBfdBG1H/rQvJVHGG6N9W9q45m79pMaLeIN6CiawCzaFPMW0WYfK849uZ3JXw+PobK2dX5Uwy5eXk9bxMN924Z5vjuBIyUb2sJsDnppfHx43PNBcWs4bg07XUaWbaTpgCpQQy6U4OR70SlYpAxB0rZQhzM8vHuUoFuf0Dxu2g4ly8Hv0hjLlHimM86bVx1dH8lcoQaMab9jjhaP5qE92M622DbCrvCk42W7jCIUFgYn9zNsj22nK91FvWdyGZkiFMKuMM8PP89ofnRGDeFQWfB/5dmvECvEqPPW4dE82NImUUzws10/w3Zsblt99OuJ33X+jh/s+AGZcgZDMbClzTODz/Dbzt/ysTM/9rp9HE8ms/x0MM6OXAFbSgKqyptqgry9uYaofmIoIVZ543Dfffexfft2tm/fzre+9a3xx/fv388tt9zCDTfcgKqqvOMd7+ALX/jCcZnjjO+Ku+66i1deeQXTnCwHd+GFF87JpKpUmQmmmWLX7s+SyWxD10K4jHqktCkUeujc/y+YVuqIZVqnRKl4IUhHMuVyVgICZLlIua//UNmTHYfiVnCvAsUPCghp4WTLuFfWEbpiIWZvFnO0gBo0JinjKG4Np2CR3zKGZ23d+HE7VSL5m32Yw/nK3FQFK15pDi8tCxO6fOG4L8BrKZfj7N79d6TGXkKWJMLSyCndJIefZzTyICvWfgZNOz4GVbZlkk+nUDUdTyCI94wzaP3a18g/+yzmwADCMPCsX4+xePG8110vWFuL7lLZ8cQg8YEcTtFBN1SWbWxg9QUteKdY3FY5cpbUB1hSP/Fzln6oh7x9yPNBCIHqN1B8OjgSK1WumAY6gC1BO/QZ6M+V+GU8xYuGxLxzC1LCcLpIc7jiFF2ybPoSBRK5MraUCAQgea5rZsGF40h2DKVJ5k2Cbp2VTYFxr44TGSEEly24jB3xHaRKKUKuQ4GjIx2GckN0BDs4o3Gy8lisEMO0Tdza1L48Hs1DvBgnXozPOLi4a99djBZGJzSYq0Kl1lNLvBjnnq57uHzB5TT4Zh8Avjz6Mt/f9n0kcsJ5LMeiO93NV5//Kv+w+R+mleK9byzFP3UNkbMdanQNXQgyts1/DcbZmi3wxWWtRKoBRpVjyFvf+tZKNnIKvvjFL/LFL37xGM9oMjO+I84++2z6+vqIvMaATAhBZ2fnnE2syqmNlA65/D5sK4dh1OHxzF5ecGTkbrLZHXjcrSjKoUWfR22lVBplYOBn1NRsxu1qnNX4ep0HJezCThanLMVwciaKT8doqejsSwxQwoANVj/k4qA3gfDhpOJ4V68meuPFCE2huCsOjpykFHMQxaNhJ0uV7ITfqDjhPtSDOZRHjbgmlEs5JZvirgRanXdKxRyA7q7/R3LwOdRcFEXqlXpuC+xSiUT5abrUb7Fk3Udn9T7NFrNUZMdjD7P32SfJp5IIRaF+wWJWXnARrStW49+8+ZjO5yAtyyI0Lw2THitilW18IRdu/6nVZ3EiIUs2U0XvlR4LgaIrCJ+GFnFXhBUECE1hoGTxlUyKPiGJBrxEXRoF0yJfttk/lkMRgqFUgWzJRlcEuqLgIMmXHe7eOsRZC6O86QhUsJ7vjvPdx7vYN5qlbDnoqsLCWh/vPLeDTYtrX/f149cpJeZgDjtZQmgKRqt/TkugpuPC1gvZk9jDPV33kCqnKlkCx6ZoF2nyNfGh0z405QLbp/tQhIJpmxMUnA5i2iaa0PDqM8t65s08zww9Q9AITrlZEHaF6cv08fzw81y16KoZjf1q7u+6n7yVpyM40RhVUzSafE10p7t5duhZzm85f9JrM5bNf/SOUHYkC9zG+Dw9qkJYc9iSKfDL4QTvnaVxYJUqpyozDi6KxSKPP/44q+dRtaTKqU08/gQDAz8ll+9ESgtFcREKnU5b6zundJU+HFI6jI7djyJcEwKLgxhGDYViD4n4EzQ1zc5FXmgKvg11pB/qxc6WUXx6JYg4IIEpyzbeDXUI3Y2+7BrshAIuHxXZlzSU90G5EyeXw8lk8J173aHSpVc1tE65G+/Iiov0gayFNZKn3JtB8euT+jAUl4osKhS2juE7vWFS9qJYGiI28Aii4EVVXRMyJar04JgeRvsfpG3pbbg8R75YOhrMcomHf/Adere9gqrruLxeHNumd/sWhjv3cM71f8ySs46fG68QglCd57id/42EGnGBlNPeC7JsY7T6CV29iOKuBMVdcZysyW9TJfpdCgsb/eNZBLeu0hgqM5Aosm80g+OA11DHFapMS+LVVXRV8J3H9nPOohp8rul/Dl/oSfD3v91BqmBSH3Dh1lVKpsOekQxfvnsn//uKFWxa8vr3jDmSJ/37HqyhPNJyAFB8leZt/8a5ad6eDkUovHfte1lft57f9/6e/an9uDU35zady0VtF02bHVhTu4YGbwNjxTGafBM3LaSUxIoxNtRvoNXfOqP55K08lmNNmzFQhIIQgpyVm9G4r8aRDlvGtuDXp5a1NtRKidSexJ4pg4vHE1lGyhatLmPSZ9JQFLyqwr1jKd7ZXIOhnPgZrCpzwxMjv8LrnjqTN1vyxeKcjne8mXFw8ZGPfIRLLrmEN73pTbhf8+Z+5zvfmbOJVTk1icUeZl/n/4dt5zGMWhRhYNt5YrFHyOX2smL55/F6FxzxeI5TxDRTqOrUC0AhFEBgmomjmrdnXR121iT/0gh27NCXgDAUPGtr8aytJXHnXoTRAcoAsphCuNygRsC9ASf9Iubo0/g2bsS9etX46/UWP0JXkCUb4Z58OzoFC9eCEMJzwNthtIA0HZTp+j88Gk7WxEoW0esm7iTmM51YxTSaU4fQJ/5QCgGaDGA6Y6T376Bu1QWzfq9mwt5nnqR3+xZ8kSj6q/S8XT4/mdgYz931PzQvX4k3ePxdw6vML+5lUXLPD+NkzUm9B07RAkXgWVWDYqh419biXVtLPFfm+e89SzTsnlSe1BL2ksxVZHQNrbJQtaWkbFcW9S0RH7V+g4FUkWf2x7l4xdRKRFJKfvBkN8mCyYKo99DutaHSHvHSk8jzg6e62bgwetgSKSteJPnrfdjJEkrAQDEqctROziT35ADSdAheOLMF+kxRhMLGpo1sbNp4xK/x6l6uX3Y933rlWwzlhqjx1KArOiW7xEh+hIAR4IalN8y4VDFoBPHpPtLlNH5j8uLfciwkklr37Dc6pJRIXt/wVDJ1icnogfJvbRpzFk1AZ77ER3b0YCgK64Ne3lwTpM1dLZus8sZmxsHFJz/5SW666SY6Ojpe/8lVqrwK2y7R0/t9HLuEx31IdURRQmhagHyhi4HB/2bJ4k8c8ZiK4kJVPVhmasrjBz0RjraPQCiCwPkteFZEKe5N4OQshFvFvTiM1uAl82g/1nAevSmE4rYo9/bi5DNABqEHEcYyPBskdR/5yIQfOr3Ri9EepLQ3WTEGO1B2VVFGMhG6gmd93aHXvN7v94H+jyl3fksSHBDTrX+UygmcjDWzN+f1piQl+fw+0umXcaSFx91KOHwmQhjseeYJhKJMCCygMn9/NEpqZISeLS+x4rzjUxpV5dihRd34z2km+1g/VrxY8XwQlQAbKfGsqsG1ZGI5bixbomg5RKcoK/LoKovr/bzUm0TKisO4oJLVaAq7qQ+4K7eTlIxmStPOa99olt3DGer8rkn3lRCCer+b/WM5dgxmDtsEn395FDtZRI16DmUNRaWR286WKWwZxbuuFi08tzuic8HlHZeDhF/s+QVDuSGklKiKSnuwndtW3caG+g0zHtNQDS5qu4if7PoJpmOiv0rZT0rJSH6EOk8dZzWeNet5q4rKqppVPNH/BFH3ZLNS0zZRhMKS8JIpX+9VFCRM6cuSMC325EuYUrI1W0ARgmdTOf5nKM7HFzZxYfT49K5VqXIiMOPg4n3vex9Llizh9ttvx5hGB75KlalIpV+gVBzA5WqY4kdaQdcjJBJPUS7HMIwjc34VQqW29k309/0QKW2EmNi7YFoJVM1HJDI3pTVarQd/7cQsiTRtijvjCENFqApabR1KMIgdiyNLRaRQEVoj4bdcihad+AMnhCB4aQcpy6Hcm8FJlznQa4rwaPg3teBafGjBojf7ES4VWbAQUyyonLyJVuNGjUxeoPj8y1GdAJaaxmDyD62lpNFKIbz6olm+O5OxrAz79/8rieTT2HYeEAih4HG30972AbKJOIZ76qyToqgIIBuPzdl8qpzYeE+vRw0a5F8exRzKgQStxoN3bS2edbWTyoZ8Lg1dFZQtB/cUfUtuXcFQFeqCLmr9LlQhCLi18YWi7VT2rP1TZA0PkipYlfF9U0flLl3BtB1ShckiJweRlkNxdxzh0iYJN0ClNMo+IMqgnX7iBRdCCK5YeAUXtl7IK2OvkC1nibqjrK1dO2UfxpFyzaJreGn0JXbGduI3/Ph1P6Zjkigm8Ope3rnqnVNmNWbCpR2X8vzw84wVxqhx14z/9tjSZiA3QEewY9oA5pywn0DfGAnTpuZVHi8lx2FvvkTJcWhx6XR4KpsjjpT0l0y+sn+QDo8x/niVKm80ZhxcfPWrXwXgwx/+8PhjB2tkbXuyRnaVKgcxzQQSZ8reCABVcWNaSUwzecTBBUBD/VXE44+Rz3fhMmpRVT8SG7Mcx3YKNDXdgMfTPleXMQmnYFfKmoxDiw/FcKE0HapPtuIFZMGZ8vWqTydy3RJKXWnK3Wlkya5Iyy6LoEUrCw0pHUrlEfA4GEv8FLcmQVMmZjryFkjwrq+fsnbbFawhIi5gVNyFJXOoeBEIJBJb5JCUCGbfjKd9boyqpJTs6/xn4vFH0fUaDL2SgXGcEvlCF51dX0H3RyklpygHcyT5VIlsssS2R0cYG9rCwrW1LFhfh8tTVWY5VRFC4F4awbUkjJMzwaksvKfrRWgKuVnTEuKpfTECbm3SpkUyb1Hjd6EgCHv0SbvP8VyZkMdg48LJwfZBIl4dl6ZQMB0CU5Q9FU0bQ1MIH6YpW5ZtpOVMKxMthKgozpVO7N9Qr+7lnKa564EKu8P8zdl/wy93/5JH+x8lVUqhKirr6tbx1iVvPaqsxUFOrz+dt694Oz/d9VN6Mj24VBe2tLEcixZ/C395+l/i0abe4GhxG1xbH+bHgzGcsiSqa6hCMFA0ydkOPkWh+VUlUIoQtLp09hfK3DeW4n1tVdO/Km9MZvwrvX///vmYR5U3AJpWMStyHHNKczvHKaEoxoxLmFyuepYv+zTd3XeQTr9C2YwBAsOooan+Jlqa/3gupj8twqUgNDHeoPlapHPAwMo9vemSUBXci8O4F4cnvlZKxmIPMTz0G/KFLkBiNNTjt07Hu/d0lIxaaZhwJMJQ8J7ZgGft1DXKQgg6Vv0JpWdGyfpfxFKTCKEgpYPiuAjGNtFUcz1a7dw0MGcyW0kmn8XQ69C0Q7uPiuLC426jUOimYW2Evffl8YZCB/pjwLElsYEcxUwWiYLmbiUxmCfW303X1hgX3LysKgV7inNQhvZInnfLmW3sHEzTEy/QEKw0W1u2w2i2hCPhtnM7eGjnCN3xPA0BF15Dw7IdYrkyJcvhtjNbqfVPv8O8sNbHyuYgz3cl8LvUCQGMlJKRTJkVTQFWNk1vxiZcB3w7siZMERwf/I5QTjHX9yMh6o5y+7rbuWXFLYwWRnGrbpp8TXMmNy2E4Lql17GqZhV/6P0D+1L7MBSDsxrP4vyW86nxHH4j632tdegCfj2SpLdYRgJjpoVLESz3e/CpE7/XhRAYiuDFTH5O5l+lysnIjIOLjo4Ovva1r/HNb36Tnp4eOjo6+MAHPsCHPvSh+ZhflVOIUPA0XK56yuVR3O7mCcekdCibCWprLsLlmvluj8fTzvLl/5d8vpNisQ8hdILBtcfEs0FxabgWh8m/MjquJPVqnJyJ4tFwLZx5U3Jf/4/o7/8vwEHXwoCgVB6gUNtNtClGQ/ztyIKDGnbhWRZFa/Ae9kfZs7COpbm/ZuTZh8loL2FraVQzRKC4nlDzBkKXLJjxHKcjlXoexy6iGpMlgIVQUFUfSnQEf3QJyeFhAtEaNMMgNZqnkEkjKBGoWUGovrWSGbUcRnsyvPRAD5uun7pGusobj/VtYT551UrueKSTnlgOy6n4WNQHXdx8Zhtv3dDMeUtq+ebD+9g7kmXkQH9Frd/Free0cPOZbYcdXwjBu85dwP6xHF2xPLV+Fx5doWhVApiQW+fdmxZMMOubNIaq4FlVQ+bxfhTbmaT05mQqKnSu12wunGhIKSnlckjp4Pb5EXOokBQwAgSM+fu+Xh5dzvLo8hm/TlcE72ur57qGKC+kcxQch/8ZTtCZLxE8jEv3NDYEVaq8IZhxcPG5z32OH/3oR3z2s59l8eLF7Nu3j89+9rMkEgk+9alPzcccq5wiaJqPlpa309X1DYrFfgyjFiEMbCdPuTyKy6ilufmmWY8vhMDnW4zPt3gOZ31keE9voNyVxo4VUYJGRQbWASdXRloS7+mNmCN5CtvGQAj0Rh9GW2DK+uuD5HL7GBz8OariwjAOZSM0zY9lZUlYj1N7xkXURCdLKB52rmtqaVv4Fkp7L8BOlRG6grEghN54+MBkpjhOqSKjO82YQtFQFLjg1tt4+pe/IDk0gG3ZZBNFFMWFv3Y1te0XHTLX0hQ8fp3+3UmyiSL+KfpKqrwxOWtBlA1tYV7uTTKWLeFzaZzWHsF/QF52TUuIf/nj09jan2IoXcSjq2xoDxN0H1mmYE1LiM9cs5r/fKqLHQNpEnkHQ1NY3xrmHed0cEZH5HXH8K6vo7Q/hTmQQ7hVFJeGdCROrozQFPznNqOeoJkLKSW9215h5xOPMNrdBVISqm9g2TnnseSsc1HU6RfZpwq1hsZltZUNoqzl8I3cyJSN3lJKyo7ktODMfD+qVDmVmHFwcccdd/Db3/6WDRs2ABVTvZUrV3LNNddUg4sqr0t93ZUIoTLQ/zOKpQGkY6KoboKBtbS3vxe/f+Y7SycCeq2H0LWLyfyhF2skj5M5oGHvN3CvDlHqTJJ7ZqjiWwGgCvQmP6ErFqCFpi7JiMUewbKyeD0LJh3TND9lM8bY6AMzDi6g0ufhXT+/9cAuV6XnZKpGewDbzuH3Ladp8Uqu/av/zcCuHfRs72LnkyOEGxbi9k8u7zI8GplYkeRIoRpcVJmAriqcuWD63glVEaxvC7N+luOvbQ3xDzeso3MsRzJfJuTRWVznP+KAXPHqhN+ymNzTQxR3xys9JQrojX58Z9TjWvb6AcrxYvsjD/H87+7ENsu4fX4QCmN93Yz9dzdjvT2cc8MtKMqpH2Ac5E01QX4+FKe3WKbNbYwHGFJK+komUV0dD0SqVHkjMuPgwjAMMpnMhMcymQy6fmLuuFQ5sZDSoiZ6EdHIZrK5bdhWHikDjI662Lu3iMezk46ODjyek8+4zGjyEb1lOeZAFjtdyQioUXdF2z5WRA25xo3tnLKN2Zsh9dtOIjcum9L5u1jqRxHqtIsXVfEcKAMbQNfDqOqJtVMWjZ5HX/+PKJWGcbkm1lDbdh4pJXX1lwOgajptq9dh+BbQs2MHmmvqenspK6aCymEyPnOBlBJzKI8dK4AiMFr8qNMEgVXeOAghWFw3e/Ui1W8QfFM7/nObsFJlhCbQajyHzWDOFVLKSn/VDIOAxNAAL937WxQhCDQcEqlw+/wUc1n2PP0ErSvX0L5m3VxP+YSl0aXzvxY18eXOQboKZXRFIICyI4nqKh9d0MiCqlJUlTcwMw4uPv7xj3PTTTfxkY98hEWLFtHZ2clXv/pVPv3pT8/H/KqcIqTTWxge+R2p1PNIKfH5FlFXdwUD/RFeeOFFcrlDLqw+n48zzjiDtWvXzmmZzrFAKAKj9VDdcO654UpgEXVPWEAohooIuzCHcpT2JvGsmtxUqKp+HDl1k7hl5ygU+ykU+3hlywdQVS81NRfR1Hg9Llfd3F/YLDCMGjrab2d/19cpFLvRtRBCaFhWGkdaBP0bSe7zMLzlXjyBAG2r1hFt8uENGRQzJv7I5B/nYtbEE9CpbT06ecrDYcWLpB/qwRzIIs3K+y/cGu5lEQKbW6cMBKtUmQmKV8c4jLrUXDLa08Xupx6nb/sWHMemtm0BS8/eRMfaDRO+X7OWzXDZxKUotLgO9Y51vfwCpXyOcGPTpLHdPj/FTIZ9zz/9hgouAM4N+/n6qg7uj6V5MZ3DkbA+4OHS2lBVgrbKG54ZBxcf/OAHaWxs5Fvf+hY/+MEPaG9v5xvf+AbXX3/9fMyvyinA6OgDdHV9HcvOoKlBEAqp1EuMjj7L4GA7pdLZhMNhFEXBcRyy2SyPP/44mqaxatWq1z/BCUxpbxJUMeXOpNAqDr2l/akpg4tIeCOjo/dg24UJDuSmlSab3YPj5HC5mlEVH7adZ2DgZ6RTL7J8+edn1RQ/H9TVXYpuRBke+g3pzBakU8LtbqUUa+WVe5IUUz+tqF0h8QZDrL/0KpacsYSXH+ilmDNxeSvyolJKSgULs+yw4tw6jHmSo7WzZZK/2Yc5WkAN6ONO6E7eovDyKLJsE7py4THZaa5S5WjpevkFnvjv/6KYzWB4PAhFoW/HFgZ272DVBRdz5rXXk7Rs/msgxgOxNBnbRhWCFT4PtzRG2RTxkxoeQijKtBs9mstFYqD/GF/ZiUGL2+DdLbW8u2X2LuJVqpyKzPgXOpfLccMNN3DDDTcwPDxMXV0dyhwqRlQ5tSiVhunuuQPHKeNxLxj/gdK0MMnEPgKBHajqEsxypT5VURSCwSDJZJKXXnqJZcuWoWknr6+BU7YPvxAVAlmeWts+HD6TYHBDRc7VqEVTA0gqbteOk0PTQng97RX5XnzoWoRsbg/9/T9m0aK/nKcrmjnh0BmEQ2dgmmmkLLP36Zd56Xe/RNE0gvUNlaDStsklEzxz588598ZbWX52I/teHCU1WgQkUoLuUll6Zj2rL2iZt7lmnx6k1J1GqAqWaaN4NFS/gerTcVRBaW8ScyA7ITtVpcqJSD6V5Olf/pRysUC48VBZojcYopjNsP3R3+NZsJivq2FezhQIaAohTcOSkhfSOXbmCvxVRyMBlxvpTJ1BBXBsG91d7X+qUqXKIY4oKkilUrzrXe/C4/HwiU98Yvzx1tZW/H4/t956K6lUat4mWeXkJRZ7BNNM4HI1Ttj5KpdKlE0NVZW4XTsmvc7n85FMJhkeHj6W051z9DrP9P4XUiIdZ1pfCUUxWLrkr6mp2YzjFCkUu8nn92BZeTQtjN+3bIIhoaJo6FqIePwxTDMxL9dzNOh6EEGA7Y/8AaEo+CPR8Y0JRVUJ1NTi2DbbH3mADZe28uZ3r2LN5haWnNHA2s2Vf592eRt79u7m17/+NT/4wQ/4+c9/zssvv0yxWDzq+RW2jpF9tB9ZqpgiyoKFHStSHsjiFKyKM7rpUOqsftdVOfHpevkFcukkgZraSVkHtz+AY1n8aPd+XskUaHPr1Bs6XlUhqKl0uA3KjuSOvhFCy1ejqCpWuTzpHI5jY5smHetOO1aXVaVKlZOAI9oSvv322xFC8OSTT7JkySF9+cHBQfbt28enPvUp/vRP/5Sf/OQn8zbRKicn+UI3wLhB2kEc6RxoMHShaiOTXqeqKo7jYJrmMZnnfOFeVUNxdwInb6K8psbayZoobg33iukVbnQ9zLKlnyKf7yST3X6gnOx+/L5lU5YpqKof04xRKo2g60emPiOlZLj3cWJDO1FUD62LLiEQmVxfPReM7O8kG4/hj0x9zd5QiNTIMPH+Xuo6FhJt9o0fK5fL3HfffXR1dSGlRNd1MpkMg4OD7Nq1iyuvvJJAYHYZBXMoR/oPvUhbgibGnZSllGA5mKN5jJZKn4dTtGZ1jipVjiXJocGKMd80lQWKx8sTwoNHVTBe8xwhBI0unb5imf3t7dQvXMzQ3t34whF0txshBFa5RCYWI9zYxOIzNh6LS6pSpcpJwhEFF/feey9btmyho6NjwuO1tbXU1tbyzW9+k9NOq+5cVJmMorgrC7TXoGkaiqIgpY2Uhz6Gup0jYI5SKpuU1OisF4snCkZ7AM9p9eRfGMYpWijuyrU6BauibX9eM3rd4VWeXu3foWth4vFHkdiIKW5fKcsIRZvQo3E4RoeeYctzf0/Z7gFhIR3B/s5/I+i+hI1v/luMOVbtssolHMdBmabUTdU0HNvGLJUmHXvhhRfo7OwkEAhgGIcyNrZtMzw8zCOPPMJVV101KxGAwrYYsmhXXNRLh8rUhBBITUFaDna2EuiqVXfwKicB091jBykoKkXdIKxOHXxoB+6jmCO56R1/wuM/+yFDe3eTTyVBgFBUatsXcN4t78AXPnFldKtUqXLsOaLgYunSpXz/+9+fVhHqN7/5De3t7XM6sSqnBqHQBkaG78J2SqjKIQUNXddxu11YVoxSaRGaU2RJ8hFac1vQ7Ty2ZYOvlminF0LvBO3kXNAJIQhc0IJe66GwdQxztACAa2EIz7o6XItnpoUeCm3AMGowyzFcroYJx6SUlM04weB63O7Duw4DJONbeenZj2E5SbC8CPwI4SCMPBnrLh77TZaLbvjnOTXI8kdr0F0uyoUCLm8lqJIOFLJl8pkyZiGHUCSJEUH9QhtNr5y7VCqxc+dODMOYEFhAJcvl8/no6+sjFotRWzvz5spybxphKGguA2u0gJRyPEgRQoAEO1NGC7twL60upKqc+DQuWcbOxx/GKpfRXnPPSCmhkMfr8VByHGDyPe7ISq+TT1XwhSNc+r4PMbJ/HyNdnUjHIdrcSvPylW8IA70qVarMjCMKLr797W9z9dVX89///d9s2rSJ+vp6VFUlmUzy3HPP8corr3DXXXfN91yrnIREwmfhD6wgnd6C29U0vqMuHQuvN086FWZsqI7zMj+jpbyPMgZpPOgujXoXiKe/CelBuPhv4CQVDhBC4FlVg3tlFFmyK7t+xvT+FYdD0wI0NryV3r7vUSqNYhhRhFBxHJNSeRhV9dLcdOMRjb176zexZRLMEGJc+14BO4iiZiiIx+nb9QLtq86adoxyuUx3dzfZbBbDMGhvbz9stina3ErDwsX07dhWaQKVgvhgjlLeQkoHx8zi8i/l5QcSjHTt4bwblmB4NJLJJIVCAa936iyPy+Uil8vNOrg4iOo3cHIWTs5EKgLUSmAhHYlwJL4zG9BqTi4PlnJPD/nnX0AWC2j19XjPPhvVP39SvlVODFpXrqGuYwFDnXsJ1tSiGZXNHce2ScdGCfuDXNJYw905mxp9stN0wrQJaCrnhiufFSEEDYuW0LBoyaRzValS5eTkscce41/+5V949tlnGRkZQUpJQ0MD5557Lp/4xCc4/fTTZzXuEQUXp512Grt37+anP/0pTz75JM8//zymaRIOh7nyyiv58Y9/TFvb6++UVnnjoSguliz5a/bt/QqZ7A5kyQQBoODztbOg4720Fp6iOdZJTglgKy58Ph/BYBBN16GYgt13w/IrofWM4305R4UQAuE+euWr5uabkNJhaOh/KBT7Do6Oy9VAe9t7iETOft0xyuU4qcwLOGV9SlMtaftQjBTdu387bXCxb98+nnjiiQliDm63m9WrV7Nx40bUKXY0hRCcee31pMdGSQ0NYpoaxbxEUWykXcLlr6NxyQUIxWBgd5JX/tDLmVcuRDkghTlVid1rx58NRnuQ/AsjSECr92KnSjiZMtKWlXOqAt/p9fjOnp9elPnAKZWIffvbZH//B5xcrhKcS4lWV0fN7e/Ff8EFx3uKVeYRTde58NY/4ZEffZfRnv04VqXcTwiBP1rDppvfwca2Dl7a1Ut3sUyjoeNRFWwpiZsWOdvh5sYore6TM2tcpUqVw3PXXXdx3XXXcfPNN/P5z3+epqaKqtzw8DD33nsv559/Pvfddx/nn3/+jMcW8vV+rU8B0uk0oVCIVCpFMBg83tN5Q+I4Fun0S2QyW8EsEIylCIyMoBQzMLoTJx/Hji5D0TTU12YoYnth3S2w+X8d9hy98TxP74+TKZqEPSoLXEVKmTgAdXV1dHR0nNSytq+lXI6RTD6HbWfRjRoi4Y1H7NKdz3fz2IM3YpVAUaZ5jZpAL17Em2752qRDvb293HPPPZTLZYLBIKqqIqUkl8tRLpc588wzOeecc6Y9f2pkiJfvf4BXHnwc6diouoEvuoxQwwZ0V6VULJ8poyiCq/58Hbpb4Wc/+xmpVIpQaHIpWT5fcfy+5ZZbZnWPm0M54r/YjTQlasioBDKOxCnbOOkyWtRNzdtXTGrKP5EZ++Z/kPr1r1GDQZRQqHJNloU1PIxwu2n4m0/irfbKnfLYlsXAru0Mde7FsW0ijU10rDsNl7cilrA1k+er3cPsy5cwDywHwprKtfVh3tNSO6nZu0qV+eREXq8dnNsPP/vXeOdYfjlfLPKOz37pmF73xo0bueKKK/j85z8/5fHPf/7zPPjggzz88MMzHvvUWWlVOaFRFI1w+EzC+kK49//AwAuVA6oOif0ojo2iGciaxZSLFmbJRggw3BqaokNmaNqxLdvhe0908ZuXB8gULTyywIJSJwGZJ+jWCLgrzeM1NTW86U1voq7uxHCwPloMo4b6+stn9VpdD6OobhDZaZ5hIaXE7W2YdERKyUsvvUSxWCQajU7oTfD7/WSzWbZt28batWvx+XyTXg8Qqm9kycar6dnVji8o0Aw3ijpxh9Tt1cgkyiSHCzQtDrFmzRoeffRR8vk8Ho9n/LylUolCocCaNWsIBoNIKRnr6WJo3x4c2yJU30jryjWT6s4nvB+NPoIXt5P+fS92rDheDoUj0aJuQlcsOOEDC3M0T2FrjFJnEqdQori1jFq3HNVTGH+O0DS05mbMnh5Sv/4Nng0bZp3tqXJyoGoabavX0bZ6agftNQEv31y1gJczefpKZTyKwmlBL3XGif15r1KlytGxdetWfvzjH097/NZbb+XLX/7yrMauBhdVji2P/RP0PwuhVtAORP6FJOTHkJlhslmVtFmDdCo7aIoqiOh5dFd02g/rT5/r5SfP9uJ3aSyI6NTGdqCTJy8MhksKvqCfkEdldHSU++67jz/6oz+adtE739g5k9KeBOZYAaEqGK1+XAtD49KnxwpdDxEOncOYdQ+ybCGUV7+7ErQ8TtHLotPfOum1B+VfvV7vlAtTr9dLMpmkt7eXFStWTD8JIVBUA81woUyhWHMwp3rwFGvWrCGTybB161bi8fh4mZSqqixZsoRNmzZRzGV54mc/on/ndsxyEYEAIQg1NLLpxrfTuHjptNPxrKpBb/BS2BHHHMxW/j7tQdwrIqj+E7s0pNSZJHVvN06ujDBU7HQW4W5F8bdDuRPKu8efK4RADYcpbtuGHY+j1Ux2h6/yxkJTBGeEfJzB8flerFKlyrGnWCzy85//HM80qpD5fH7WHlLV4KLKsSPeCd1P4HhqkEJHOajI469DFhLYpoMmR1DUCIpRqddXrBzFomBnz1LW286kRWi6aPLrlwZwawo1PgNPbhC9nMXSPBhCoWDaDKYK1PpDRCIR4vE4e/fuZf369cf88ov7kqQf7MHJlCu74kjyLyrojV5CVy1ECx9bl9sV6z7I0w+/gGkM41huhDRAOAgtj2OphFxX0rh49aTXWZaF4ziTVJsOclBX//U8SqJNPtw+nWLOwhs0kI5T6Q1wHITbQ7Eo8fh1Ik2+8XE3bdrEkiVL2LdvH5lMBo/Hw4IFC2htbUUAD/7g/9Gz9WW8oTC+SAQhBLZpkhoe5OEffofL/+wvCTc0TjsnrcZD4Pz5cwB/Pex0muwjj5J74nGcTBa9rQ3/RZvxnnEGYhpVHjtnknqgB6doodZUMjpOwUaWkwgtBK7F4CTBOuQnIzQNWS4jp5D8rVKlSpUqpz4XXngh99xzz+s+ZzZUg4sqx4z0vmdQEyPEnCBSpNFUDb/fj98XwVEDUI6jiyK6UsLGjSHT6EqWIe1sdvW10bA/TfOS8IQxt/aliGVLtIQrkberWOmx4IBpn6EqFEyHXNnG76qUR3V2dh7z4MIczZO+twunZKNG3Ailsh0vLYfyQJbU7/YTvXn5Mc1g+AMLOOv8/+CVp79A1nkFSQ6kwCnU0lD3R5y2+YNTZiZ8Ph8ul4tyuTxlgGGaZkWC93U8Stw+nUUb6tj2aD9kEojYME6pBFJiaR4sb5Slly7E5Tn0NSWEoKGhgYaGyeVag3t30b97O75wZII/h6rrhOobSA4Nsfupx9j41htn8jYdM8zBQYa/8EVK+/aBoiB0ndK+feQee4zApZdS+4E/Q0zRM1Tak8DJlCufqwN/L+WAMhB2AbQQaK0Tggs7m0UNh1Gj0xs4VqlSpUqVU5c//OEP8zZ2NbiockwYHBxk+zPPcFqphNQkQiiYpkk8HqdQ8KDKNtzCwUMKjzOCI3RMEaTXdSVd3huxRyT9uxKTgouS7eBIUA8s1oW0ka9aEAvBASfwA2VWioJlHXuH5cLWGE7eQq1xT1iwC01BC7sxh/KU9qeOuYdCMLyE8y//DulkJ/HBXSiqh8aOszFc08utulwuli5dyosvvojH45mgCiWlJJPJUFNTc0QKcms2txB78iX6egtI4UEYXqRQUByL2tGXiT7wANYFn0KLvP77MrBrJ3bZxJhCKlYIBd3tpvuVFzjrLTeccH0GUkpGv/avFPfswWhpQeiH6t3tTIb0PfdgLFxI6JqrJ73WHCuAZDxgBVBDIRSPB6dQQFFMUA+9f06phFMoEL7xBpQ5bkqsUuVExrFture8xL7nniI5PITh9rBg/eksPvPsqhFglSpzSDW4qDLv2LbNww8/TMkMsUH34hVlyqoPVVVxHIdCoYDqSDT8JPVW9nnehhQ6aW0RphIGQCgFSvnJQUFr2IPHUMmWLAJuHUv3VbIXUoIQWI5EUwQuraJmZFnWcWnoLnUmEYYy5aJWaAo4DuW+zHEzaAuGFxEML5r2eG5gG8kn7qX41CsowzbNrW30tDQzlkjgcrkwDAPbtikUCvh8Ps4///xJUrSlUomenh4KhQIej4f29nbE6BgLX/kREa2eWGgZZVy4KFKnDhP2jWDt7iNz3/1Ebrn5da/BMsuHPa6oKlbZRDrOtCVGx4vi9u0Ud+xAr6ubEFhI0xz/d+aeewhecfmk7IVQFQ7U2R1CUTA6Oijt24dTNhHCxEmncfJ5ZKmEZ8MGQm95y3xfVpUqJwy2ZfHEz37EvheeRjoOustNLplgrLebvc89xUW3vY9o8/EriaxS5VSiGlxUmXf6+vqIx+P4IgsYZhmtuVewhIGj6BX/AkVBsVIIbPrdlzPmmujTcDDz4I+4Jo29pN7PmuYQz3TF8RoaBU89ntwgilPGUgws26Eh6MbQFHK5HIZhsGzZsmN16YeuwZaHOpOnRFSec4KRy3XSs+Pfie1/AMcuIs5QMGI+Ai8OsH5HgL7TT2ewtZVSuYyqqixfvpz169dPKFsyy2Uef+A+Xt6ylaJpoqgqhttNOFrDatsmnElT2x6mju0TT65rCLebzIMPEr75pmmzDY5TIpl8DiX8PMEl3SiqiZNtBmei2k25WKBh4ZIT0lG43NmJLJcRB0wCnXwec2gIO5kExwEqGYzijh141q6d8FqjxU/+RQVpORPK6tRgENeypZgDaezYy0jLQquvJ/DmNxG86qqqkV6VNxS7n3yUvc89hScQxPUqM07HsUkOD/H4z37I1R/++An5/VClynxw3XXXHVEW/5e//OWMx64GF1XmnWQyieM46LrO9prLcTlZaopdCAscoSIcE1ModMnNdHPJpA9lMWehuzTaVk6uDxdC8IGLFjP86yLd8Tw+Q8d2txLJdoPMEnC5qHFDPB5HVVXOOOMMmpqOvRGa3uijtCcx5THpSBCgn2DOz7lcJ7t2fZrswCuIgo2u+nFUh1JrAaveouaZIEueeprT37Uc15VXouv6JNWJQjbD/3zrm+yPJ5ESNBxsKSnms4yVSjxlO6yNhFk0jZa+4nbjpNOVhbdrcnBZKPSwZ++Xyef3YetlAgtiSHsA7BCl/tNw8geUkPIOLWIxS90bSd2zH6M1gGtpGMV1onwFHvqCt7NZSnv3IkulSpZCVZGmiUwmGfn/vkrz3/8d+qs+w65FIfQGL+XBHFrYNR5gSEciyxpGSx2hP/0T1MB7UQOBKfs2qlQ5lXFsm11PPY6iKhMCCwBFUfFHosT7exncu5uW5SuP0yyrVDm2bNiwYd7Grv7KVJl3DhrXSSkpq36eq38bDfldNOZ34LJzJAnSYyzDF72Z7O4SWrGEy6uBhGLWxJGSlZuaiDZPLZO4oNbHF29Yy10vD/DQzhFi5SaUWj8r3Sm8VgZVEbQ2d7Bq1SoWLVp0XOrtPatrKHemcPLmBL8EKSV2qoQSMHAtO7Fqfvv6f0gh0406IlB0D0IqqJaCklUxfUVSa4eo3ddA4cGHqLnuOpQpmruf/MVP6B2LIXQDz4EAQgKObWFl02C42VtXR0epjDrF38UpFtFqaxFTjG1ZOfbs+QK53F7c7mYUtwthR0kM9aG6kuhNz5DZeSaeVAMLxRp83jDulJtCMkZhWwzteQ+hqxeh1x7/oM61fBnC7cLOZDD7+yuBxau8PKRpIoJBzP5+4v/1XzR87GPjrxWaQuiqhaR+tx9zOF8p+0KAAMWvE3xTB6728HG6sipVjj/5dIpsPIbLO3W2Tne5yCUsEoP91eCiyhuGz3zmM/M29hEFFxdffPERLcgeeuiho55QlVOP1tZWXC4XhUIBr9eLrRgM+Ncy4F+LlJJEIkF7ezubr1jLrieH2PfSKMWMCQKCdR6WntXAktPrD/sZbAp5eN+Fi3n3eQspmDZeXUVTlXE5VF0/voZQrkUhvGfUk39+BKtQQLhUcECWbRSvRvBN7ai+mc3RtkwG9+win0pieLw0L1uB4Tkyh+7Xo1gcIJV6EbXkwpFyQo+CQKAVDcqBPNYCgdI9hjUyitE6sV45MdhP566d2LoXQyivej2oqoZj20jHJuPxMJZM0uCdGDxKy0IWiwTedMmUf/tE4nFy+U7c7lYUpfLe+UIRVE0nmxjDUkbwRWIsKV6Jxx3A0xxGUSrXIW0Ha7RA6nedRP94xbj08fHCtXQpnvUbyD78B5xMBmFUXMKREmlZIARGYyOoKvmnn8EcGUGvrx9/vRZxE715OfmXe8k9sxsnn0f1KQQuXIX7NSIIpxpO0cJOlkAVaFEPQj2xmvWrHH8URTngi+NMeVxKieSQjHaVKlWOjiMKLt797nfP8zSqnMqEQiGWLVvGli1bEELgdlcUkxzHIZPJYBgG69evRzc01mxuZfm5TWTjxYqcaa0bdQqDtenQVQX9Vc8/3kHFQYQQ+M9vQW/2U9gWGzdpc62pwbOmFr1+ZkFB7/YtPHfX/5AaHkI6DgiBLxxhzcWXsvL8i446O1M24zhOCUW4cCSVdMOrhlRsFUspYbnKGIoXoR1anJtjBcz+LPE9+9FNN8JQmWo2qqZh2xaq34dZKmNmshVVKE3DyWSwE3GMJUsIXD61C3ky+QIgxwOLg7h9ftw+P/mChuF2EyrUo9V4Jqp0qQpqxIU1VqC0L4ln5fE1khNCUPehD1La34k1+gJSOgjLqnh+qCp6UxNqbS2Uy1hjlWDu1cGFlJL0vXeT+NGPsFNJoLKQyj7gI/CmN1Pz3j+ZMvtzMuPkTbLPDFHcGUcWbVBAjbrxnVaPe1XNCacIVuX44QmGiLa2MbR3N27f5OxFuZBHd7loWLTkOMyuSpVTjyMKLt71rnfN9zyqnOJs2rQJ27bZs2fPuLsyVDwTzj33XDo6Osafqxsqkcb5dYqVUh6QwS3gdrupqZn/xYgQAvfiMO7FYeRBA8FZMLhnF4/86HuUC3n8kSiaYeDYFrlUimd//QsEsPKCi49qrprqR1F0hE8BTaVsmZQVBYlEEQJDBSEFxAsYC1ejNTTgFC3SD/ZQ6kwhyzZ6QbJcWUdc7semhBCTG9YdBK5IlMa3XIvywAOYIyPgOCheL74LN1Pz3j9Bm8aLwZFlBNNnHFRFxykWUQx1apUuVUE6UO7PHpfgolgcIJfbhxAKfv9yjJpaat/3Pga7P3Wg+V+iuD2oNVEUnx8BOKYJqorimSghm3v0UWL/79uAQG9pRagVdTQnlSL1m18j3G5q3vPuY36N84VTskje1Um5J4NwqSg+HenISjbq/m6cvIXvrOnNEqu8sRBCsPK8zYx2dZJLJvCGwuPfCWapRD6VpGPd6URbXl8+u0qVKq9PteeiyjFB13Uuvvhi1q1bR09PD+VymUAgwMKFC/F656aU50gZGhrimWeeYXBwEMuy0DSN+vp6zjrrLFpbW4/JHGYbWEgp2fLQvZTyWcINTYdM01SNQLSGTGyMLb+/n8Vnnn1UJVIeTwd+33KSyRfIuQ0KxQI4goPpi7KnjJ7UMcZ8hG69GhxI3b2f0r4Uik9H8etoJYknq1BrBxhWHZDlCdkP27aRqkFbezsLrr0Wed11FHfuQpomekvLpDKr1+LzLiYWe6Syyy8mZ7csO4+/tPKwKl0CqKRmjh2mmaC7+9skEk9g2VkANC1Ebc1FtK77Y4yODuxkEr1x8uLYisdxLV+GsXDh+GPScUj+6k5k2cR4lbeIEAI1HEbaNpn77iP01rdMG6idbBS2xij3ZlBf1cAuAMWlYmfKZJ8ZwrUsghaaLAJQ5Y1Jx7rTOD0R5+X7fkdyaPBAmZRE0TRaVqxh001vr2a7qlSZI6rBRZVjhhCC2tpaamtrj9schoaGuPvuu8lms/h8PrxeL5Zl0dfXRywW47LLLqO9vf24ze/1yIyNMtLViTcYmvKH0BsKk4mNMbBnFwvWnTbr8wghaGl5O8N9z2J586hShZIEYaP6LBxHMLwjQs2FF+G78ELK3WlKXWmUoDHev2C4PRheLx1ZP1l3ibS0EdgIwHZsLBSikTDnbtqEEALh9eI9/cjnXFOzmcGh/6FUHsZlNE54P8rlGKrqoca3GVm2p3z9uEpXw7ELbm07z+49f08q9SKGHsHjbgckpplkaPhXlM04DdddS+zb38MaGUGtqalkISwLa2QExeUifMMNiFfVhpt9fZhdXajTGA2q4TBmXx/FLVvwb958jK50/pBSUtg2BqqY0tFe8evYsSKlPUm0Myc7uVd5YyKEYM1Fb6Z15Rq6Xnqe9NgIhttDy8rVNC9biVpVUatSZc6Y8d2Uz+f5xje+wa5du7Dtyo+2lJKXXnqJF198cc4nWKXKXCGl5JlnniGbzRKNRscXo6qqYhgGiUSCp59+mtbW1hO2sa9cyGPbFi5t6rIxVdNASsxi4ajPpckOxl7uwNNmYoQKIC2kJbBzPkq9bZRLtfRYRdY7DqX9KbDlaxqjBZGmZpw+izOKbnZrQ/QqIziAArTW1XL1H7991sGmx9NKR/v76Or+dwrFbjTVDwgsO4uiGDQ330JN/Xmk9uzDzpZR/Yd6DqSU2MkiatA4psaFsfijpNOv4HG3oCiHdtUNowbFdpNIPEndhZdTY72L5C9+idnfdyDzItDq6oi+41b85503YUxZLiMdB2W6xdGBz7JTPrzJ4EmDLXFyJkKfuiTu4H3t5E6R660yp4QbGtlw+WSn+ypVqswdMw4ubrvtNsbGxjBNE03TOOecc/j3f/93br/99vmYX5Uqc0Y8HmdwcBCfzzdp118Igd/vZ2xsjOHh4aP2wrDGChT3JrCzJopbq3gRNE0+70zxhiPohguzWESbSp61XEJRVbyh2S+YnVIJJ59naM9OsgNuVOtinEACoRWRtoGdqwVFxRstkh4dJjk8iFq0maprW9MN6joW4h9OESz58fqzeCK1LDntdBavP/2oDavq6y/H7W5ieORu0qkXkThEgudSX38FkfA5APjOaSL31CBWrIDQFZAgTQcloBO8tGOCNPDrUbYcnt4f4/G9Y6QKJk1BN5uX17OudepM0muJjT0MiAmBxUE01Ue5NEoi8QSLb/gr/JdcQv6553CyObRoBM8ZZ6L6JweVWkMDaiCAk82iuN2Tjst8HuFyoTc3H/F1ntCoAmGoyKw55WEpK2VuwlU1Q6tSpUqV48GMg4v777+f7du3s3PnTr70pS/x5S9/mfXr1/P9739/PuZXpcqckc/nsSxr2h4PXdfJZDLk8/lZn0NKSfbJAfIvjFQUbA6e+4Vh3MsiBN/UUVngzhJvMETb6nXsfupxXH7fuLTq+LkTcaLNrTQtmbkLuTk8TOquu8g9/AhOocCIoWIZEicSxc7VTXq+oggc6ZAeG6UwOIoelxTiOQyvF184fECVRSCEgiFceBpauOy009AbfRhtwTmTDA0G1xEMrkNKBykdFGXi15rvrEaMJj+F7THMgSyoCq5FQTyratGibsyxAuXuNNJy0MIuXItCU+6KpwomX7p7B891J3AciaYITFtyz7ZhrljTyJ9ftBjtdZTNymYcVZletUkIFdOsmC1qkQjBSy993etXAwF8F15I8he/QAkGJ/iNSNvGGh3FvW4t7lWrXneskwEhBO4VUXJPDiAdiVAmfo5kwUK4FFyLwsdnglVel+TwEPtffI5Yfy+artO8fCUL1p02Z1LaVapUOb7MOLgIh8Ps3LmTc845h5dffhnHcbj44ov5sz/7s/mYX5WTCLNkU8qb6C4V1wx2g48VbrcbVVWxLAt1ih3zg4+7p9j9PVIKW8bIPT2E0BXUGvd406As2hS2xhBujeBFs1ckyWazeDqW4GzdzkBXL75SHsMwEH4/JcfG7Q9y5jXXzzgjUO7rY/j//l/SfX2MNDSQbainZJYpF1Jkd+0ksGwFinvibnsxl0NVNZ76xU+QaYt1xgWoQiGfTFBIpwjU1OL3hbHGSsiyg3Qk2ccHQBHoDT5CVyxAi87+vX4tQihTNnYLITDaAhhtgQmPO2Wb1D1dFPckDvVlCFDDboKXtOFaEJrw/G/+YR9PdcZoCnrwHCj/klKSLlr8+uUBWiMerj/98IIALlcD+XzntMclFoZRP+3x6YjcfBOlPXsobtmCMAwUrxdZLmPncujNzdT+6Z+eUs2q3rV1FHcnsGJFVL9eyVLIijytLNl41tWh1R1/c8Qqk9n99OM895tfUsxmUFQNx3HofPE5tj/yEJvf+V4ijadIhq1KlTcwMw4u/vZv/5Yrr7ySoaEhzjvvPC677DIcx2HNmjXzMb8qJwH5dJldTw/S9UoMs2SjqIKWZWGWn9NEtGl+JWVnQm1tLfX19fT391cW5K9abEkpx3sxGqdQ6TkSpO2Qf3EEYEJ9vxAC4dGQtkNxewzfmQ0Tjh8Jtm3z7LPPsnXLFrIDA9gWmOE6cmYZf2wIT7yXuoZGzrr13TStmPkOdew736UnkWTLunUUDgQmEijbUTL5DEp/P4HFi8afb5VLlAt5hFAwi0VC9Q3EzREaiq1oiotSOUtuYARNd1CEBrIIPh012giWQ3kgS/KufURvXo7inp9GSulIpGkjNHVSlkRKSebBHgrbYiheDSV6IBC0HOxkidQ9XUSuW4LeUPn89sbzPLFvjKjXGA8soPK3DXl0ciWLu14Z5Jp1zRhTNBkfpLbmIhKJp7DtPKo6cZfWtNIoipuamgtmfK1qKETjp/6WzL33krn/AaxkEsXvI3D1VYSuuOLUKYk6gBo0iLxlMenf92IOZHFylRIpxaPh3diI/9zmUyqYOlUY2reHZ371cxzbItx46G9kWxbx/j4e/dH3uOrDH5+y5LNKlSrzx5NPPsknP/lJent7x0tLD9LZOf2G2HTM+Ff9fe97H+eddx4+n4/vfve7fPWrXyWbzfKRj3xkxievcvKTS5V45Me7iQ1kMdwqukvFtiT7XhxlqDPNeTcuob4jeLynCVQWgmeeeSaxWIxEIoHf70fXdSzLIpvNous6GzdunDKrcSRYsSJ2soQyjdO24tWx40XM/izq8plJgj777LM8//zziFQKbzyOYhg4qkbW48NpXsiKkREaOgcwnngGVs4s0C/39jK4aycvLVyAqaj4LZuDy+MSkPAGGERgDw5geL2YpRJSSkL1jaRHRwjU1iEUhTFjEFMpE04EcNseVMUHKDi5EWQhjh2TWIMDuJYuQYsEsEYLFHcn8K6bXHJ1NDhFi8IrY+S3xw40/iq4l0fwrq9DC1cyJdZopSdG8ekonkNfg0I7YK4XK5J/aZTQ5ZXgYudQhmzJoj06ddlG2KszlCrSE8+zpH6ySddBotHziETOJRF/DFX1o+shpJSYVgLHKVFffyXBwNpZXbcaCBC+8UZC11+Pky+guF2IU1gBR6vxELlhKdZwHiteRKgCvTUwY6f7KseO3U89TrmQJ9zYNCH4UzWNQG0d8YE++nZsZcH604/jLKtUeePxjne8g02bNvF//s//wZiD4H5WvzyrDtTuulwuPvvZzx71JKqcvGx/tJ/YQJZQrRvlVfXmbp9GarTIC/d0c9ntqyccO560t7dz2WWX8fTTTxOLxchkMqiqSjQaZePGjSxevHjWY0tbgjyMh4V41fNmQDabZdu2bWiKgpZIIFUVoWqoQNB2yGgq/dEamlNpMg89RPiG61HD4SMe3xwYpMvjoaRpBG17Ql+2C0FUSjJuL6ouUVWFcFMLS848m8TgANtHHzok4SggUepmsGcXAVcTHZHzUYSCXs5WFIukg5PNUty5C9fSJSA8lDpTcxpcOHmTxK/3YfZXeisUQ0EWbXLPDlPamyR87WL0em+lx6LsoEyRQRJCoLjVihmgaSP0iiHda0zKJ74GQeWvevi/raIYLF3yv+jvb2d07H5K5VEADKOWhvqraGq6YcrSrpkgFGXKxu+jpTee5/7twzzTFcd2JKuagly6qoE1LaHXf/E8IYRAb/Shz7PpZpWjR0rJwO4dGF7vlN+Rmq7j2Daj3furwUWVKseYeDzO3/3d300wND4aZhxc2LbNv/3bv3HnnXcyPDxMY2MjN954I+9///tnveNb5eSkmDPp2R7H5dUmBQ9CCHxhg+RInpGeDI0Lj98C5LV0dHTQ1tbG0NAQ+Xwet9tNU1PTUX9+tYgL4VFxihaqPnnRKks2wlDQamdWC97b20uhUCCgqpRNE8V1qPdBAC7bYUTXKEejaP39lPbuxXvmmUc8vjB0hkIhDMeZcvFsOA6aorDgjLO58Lrrxh9/7je/nPzkeJaFkQsJe9vw6FEUoaEE25BWDlmIIaWJNMuYPT3obcun9aCYLdlnhjD7Mqhh9wQPBMWR2PEi6d/3EL15OdJyKtc+TSBYce+WSEsidFjaEMBnaKSLFiHP5J3xZMGkzm/QGnn9hlRV9dLe/ic0N99EvtCNQMHrXTCpTOpE4tmuOF+5dxejmRJuTUERgn0jWR7aOcx7zlv4ur0mVaogJVJKxLQh+sGnHVtTyypVqsBHP/pRPve5z3HHHXegzUHGe8Yj/OVf/iX33nsvH/zgB2lpaaG3t5d//Md/ZNeuXXz1q1896glVOXnIpUqYZQePb+qPkW6o5Owy+eSJpzevKArNc1yHrrg13CtryD0ziOLWJqhCSdvByZZxLQ7PuNHUNA/Uk093XsAWAktR0JAwwx9nY8UKMAwwTdAmL5ylaSK8PpTXuDvXL1yMoqpY5TKaYaBIhXbtDAKuOspmlpLI4tECgEQYARAqMlNxxnVKZZxcHq125s3L0+EULYq74giXNslcTSgCJWBgDeUxB3KowUqAJm0HMUVWzSlZaBH3uJzpwlofZ3ZE+MPuUTy6OqGvIluyKFk2V65twj2N98JUaFqAYODE71WL58r88327iOfKLKjxohwIyKSUjGbLfOfx/Syp97OuNXx8J1rlhEYoCg0LF9P9yot4Q5M3m2zLQigK0ZbZC17k0yn2v/gc3VtewiwWiDS1sOiMjbQsX1XtwalS5TB0dnbyu9/9jgULFnDOOecQDB4qZ//Od74z4/FmHFz8+Mc/5uGHH57QwP3mN7+Ziy66qBpcnCpICckeKCbBHYZw+wEjr4nohoqiCGxbTvlBcg6U/6jGiVESdSzwb2zEGslT7k6DIhC6grQkWA5avZfAxe0z/pELBAKVBblhIDQNaVmIV2VGTCHQpURPJlEDQYxFiw4z2mQ0j4e65mZ6+/pwl0sIwwBEZafRLOMAasBP5DVmdy0rVlHT1sFoVyfBujrCThMBvZ5CKYElLRxb4lUPlKvYFkLzIHQvyBKoXpAW7hUz6z05HHaqhCzaKN6pv9aEoeBkHexEEdfSMGrQhZ0uo4ZdE/4mTtkGW+JZUztB5vSDlywhni+zrT+NEKCrCiXLRlUEb17ZwA2H2b2XUlLe30X+2Wdxcjm0mii+TZvQ6ua232Q+eGT3KMOZEu2RQ4EFVLI+9QEX+2M57ts2fNIFF1JKtg+m2dafxpGSRXV+Tm8Pv66ccJXZs/TsTfTt3EY+lcQTPOQNI52KpHW4voGONetnNXZiaIA/fP/bJIYGUFQFVdWI9fex/+XnWXHuhZz11hsmSHdXqVLlEAsWLODP//zP52y8GQcXCxcuZGBgYEJw0d/fT1vb7HcbjpStW7fynve8h71793L77bfzD//wD9XdiLlmaAs8+/9g8CWwSqC5oOk0OOu90DhxlzVQ46am2cdQVxrDrU76WxSyZbwBg8aFJ0ZD97FAcWuE37KY4o44he0x7FQJNaDhWVmDe3XNrJpN29raCIfDJBIJfNEo1vAwQlVBUbEBUxEsTGdRkwl8116LVlMz43Osu+wyhn75S8qJBHqhUAkmpQRdp1hTQ6ipaVI/iqrpXPj2d/PwD79DrK+bZmUhDhILB0WCq5QHJ1UJUA+4TAtXGBQbHAu92UafQzUxoSqVmMiRUxdeHEzoaAqKSyNwUSup+7qxY0WER0OoAqdog+3gWhTCs3ZiMFXrd/GF69by6J5RHt49SjJv0hx2c8mKBs5eGJ12USrLZcbu+BbZ3/8eJ58DoYCUJH7yEyJvexvBa689ob/Hdg9nEBJUZeo5enWVV/pSx3hWR8dYtsQ/37ebl3qTlKxKn5GqCBbX+/nYZctZXDd9U36V2dO6cg3r33wlrzx4D8mhQTSXC+nY2KZFoLaO89/2LvRZSIE7ts1jP/kBicF+QvUNE6S4i9ksOx57mGhLK0s3bprLy6lS5ZThM5/5zPj/Hxsbo/Y1m4kzZcbBxdKlS7n++uu5/vrraWtro6uri1/96lfcdNNNfP7znwfg05/+9FFNaipKpRLXXnstl19+OT/5yU/4i7/4C773ve/xnve8Z87P9YZl4CW455OQGwFfHbgjYBWg6xEY2wlXfBma1o0/XQjByvOaiA3myMRL+MIGqqrgOJJi1sQyHVZf0HBCel7MJ4qh4l1fh3d9XaXG+CgXjpqmcf7553P//feTsW20SATSaSxVYqkqkXSahd09eM7aSPS222Z1jqVLlzJ47rlse+UVCvk8BiAVBdMw8AUCbN68eUr/j1B9A1f++V/Rs+1lzPviaAUVv2OgZ7Ioto3jxBFOGWEEEJoPVA1Z6EPm9xG65hNzuqhWo260Oi/mYBbFNfmrzcmbKB5t3O/CvTSC4tHIvzRKqadioqeGXXhX1+BZX4diTN7l9Lk0rljTxBVrjtzBPf7DH5L+3e9Qg0H09o6K5K3jYI2NEfvOd1FDIfybN8/+wucZRYjDtqlL4GTa7C9ZNl/43Q5e7ElSH3DRaFQyV0XTZudghv9713a+cuN66gKTXdSrHB1CCNa9+QoaFi1h3/NPM9bTjWbotK1ax6IzNuKPzC6TObBnJ/G+HvzRmkkeP26/n1I+x+6nHmPJWeee0IF8lSrHi3K5zF//9V9zxx13UCgU8Hg8/Nmf/Rlf+MIXZqUeNePgwu12c9NNNwEwMDCAYRjcfPPNAOzfv3/ebty7776bVCrFP//zP+P1evnCF77ABz/4wWpwMVdICU99E3KjEF18qAxKM8AVhPg+ePqb8NavTyiRal4aYeM1C3n5wV6y8dL4UG6fxtrNraw879TS158pc3U/dHR0cPXVV/Pyyy/T29OD6ffjymZZksuzIhql7o+uw3fepgnN3jNBURQuvPBCmpub2bFjB2NjY6iqysoFC1i9ejV1hynf0d1uFp9xNsmhToo74ygttZT27MaOJ8CykXYaWcqg+Bpw8nuxB/9A+PobMBobZvt2TIlQBN7T6kmP5LHTJRS/gVAOmBgWLGTJxruxcUL2yGgNYLQGcIoW0nJQPPqcOYcDWLEYmfvuR/H5Jih4CUVBr6+n3NtL8s478V1wAUI5MVfoa1pC3L11ENN20F8TRUgpyZdtzlowd+Vt880z++Ns6UvRHHJP6JFx6yrtUS89sTwP7hjmjze2H8dZnroIIWhcvJTGxUvnbMx4fx+OY6NP8/3n8vlIDg1RyKTxBk8ccZEqJyeG7xoMz9xmNy01C3xpTsecCR/72Md47rnnuO+++1i8eDH79u3jYx/7GB//+Mf52te+NuPxZhxcfPe7353xSeaCl19+mXPOOQevt6Kosm7dOrZv3z7lc0ulEqVSafzf6XT6mMzxpGZ0F4xsA3/95P4KIcDfAENbYWw31C2fcHjB2lpalkXo352gkCmjuzSal4bxBqtGSHNJU1MTTU1N5HI5TNPE6/XOiR71QRRFYdmyZSxbtgzbtlEUZUbBkXt5hOLuBKDhWbMGc2gYs68PWS6D7gZhg9lP+KabiN5664zn5zgOUsrDqnq5l0eQBYvsUwPYiWLlQQnCpeI9rR7/uVMHu/Nl5FfYsgU7nUZvaZnyuBqJYHZ1Y/b1YbSfmIvZ85fW8t/P+dgfy9EW9oyXfzlSMpAsEPHqXLZ6dsaTx4Nn91ekdKdqvlcVgaEpPLJntBpcnERUNhGYPlMsJQhO2AC+SpXjzU9/+lMee+wxli1bBkBDQwPf+973uOCCC45NcHG8SKfTLFy4cPzfQghUVSWRSBCJRCY894tf/CKf+9znjvUUT27yY2AVK0HEVOheyI1V/ntNcAGgu1QWrD26Gr0qR4bPN/+a/rOR5XUtDONeHqW4I4YsKWg1daiRGux4FqdsYTRKwjf8LfoMazn7+/vZtm0bvb29ADQ2NrJy5UoWLlw4aSEhRCV74VoaprQ3iZ01UVwqroWhGUsAv5Z4rsye4QwAi+v91PpfP0skTbOysJlmUSM0rRI0HVAEOxHxuzQ+edUK/v63O+iJ54FKqZTlSGp8Bn/xpqWHNQ480cib9oTG9NeiKYL8HEskV5lf6jsWoek6ZqmI4Z58nxdzOZqWLsftO3k+p1WqHI4777yTv/qrv6Knp4c1a9bw4x//mJUrV866NzkajbJr167x4AJg165dRKOzy0qfNMGFpmm4XpPydLvd5PP5ScHFJz/5ST760Y+O/zudTh+ThvOTGlcQVKPSxG1MobdvFSvH3XPQnG2VoOdJSHRXxmw9E2qWTKlIVeXkQaiC0KUdaFE3ha1jOFkTBGgNYbxra/GeXj+l7Ovh2LZtG48//jilUmn8/t+9cyfbX36JWl1hUVMjCzecQaB2EUOdGayyjTdk0Lo8gnfD3Mjc5ssW33+iiwd3jJAsmIAk6Na5eEU97zlvIf4p+jsOYrS0IFwuZD6PeE1QKB0Hc3AQHIf0/ffj6evDu3EjiufogqD5YEl9gH9522k8tmeMl3qTmLbD8oYAFy2vpzE08wbc40l71It90HNhiu+cgmmzsKZqyncyUb9wMfULFzOwaztKbT2aXil9lFKST6VQNJUVmy6s9ltUOSXYt28f73nPe/jmN7/J5s2b+fCHP8ztt9/OQw89NOve5L/7u7/jbW97G+94xztYtGgRnZ2d/OhHP+L73//+rOZ4RMHFJZdcwl133YXX6+Xiiy+e9gZ96KGHZjWJIyEajbJ169YJj2UymSnLQlwu16RApMrrUL8Koosq5VGRBRMX+lJCdgQaVlWedzT0vwAPfxkSXSCdytiGDxZuhs2fAFfg6MavcsSUSiU6OzsZHBxESkl9fT2LFy8eLz2cDUJX8J/ThPf0eux4pSxJq/FM8Pw4UhKJBE8++SSO4xCNRpGOQ3ygj3ImjYVgUCgUh55n+6NPoGjt+KIXoag6EvAGDTa8uZ2F644um2baDv9wzy4e3j1K0K3REnIjhCCZL/M/L/QzlCry6WtX4dL+f/bOM0yO8krbd6XOaXIO0ihHhBAIAQKBAJPBxsYYDGbtXbDXOHudjSPYXq/Xn9PaxgmnxV5HchQCJJBQznFyTt3TOVX4ftRopNHMwETFuq9rLs10dVe91eques97znme4TM99rlzsc+eRXLnTmwOh6nyBejJJOnDh9EiESSvh8hjjxF5/Als5eUUfOLjOGYPzQ6ebHwOhWsXlnDtwtE3s5+KrJxZwF+3tNAdy1B4XNN2JJVFFkVWz5vcfiCLqUUQBC5+9128/Ltf0NVQD4aBIIromobN6WLJ1ddTtWjJyR6mhcWksG/fPr71rW8N9Dt/8IMf5LrrrptQb/Ktt95KVVUVv/zlL3n55ZeprKxk7dq1LF26dFxjHFVwcffddw9M4t/3vveN60ATZdmyZTz88MMDf9fX15NOp8edsrE4Dkk25WZf+Ar0NYKn2KyTz6Yg1mEGAOf9C0xEJ7y3Fp77otk07iszZW4NA1JhOPAk6Bm4+iErg3EC6Orq4vnnnycUCg044u7du5etW7eyatUqKidY/y/aJMTiia3+Hjp0iGQySW5uLoIgEO7uIhEJIysKiiiRNiBlDyD1hjCSh3F6A+QUr0TXDOLhNJuerMfuNPt/xsumhiCv1fZQ5LXjPiZDkeex47LJvFEf5PXaXi6bPXyWRBAE8u+9j84HHyTT3IzkcoEkkamvR0+lkAIBHHPmmP4l2SyZpiY6v/0dSr/1EErh5BkMWhylOt/Ney+s5lfr6mnsjeN32hAFCKfM0rRrFpSwfPrY5ZwtTi6enFyuuu+jNO/ZScve3WRSSQJFJUw75zzyyq3KBYvTg+N7hIdbLL/++usH/X3gwAFmzpw5pt7k4Vi2bBnLli0b58gHM+rgYrjfTyQrV64kEonw61//mnvuuYcHH3yQ1atXj6s23GIEpl8Gl38J3vi5GWBoKkgK5E6D8/8Npq2c2P73/ANinWaGROhfyRYEcAbMvxvWmz4bx8jdWkw+qVSK559/nmAwSCAQGPgO6bpOX18fL774IrfccguBY9SNTgY9PT0DTeWaqhIP9yGK0oARlmAYJDWDgGJH11VivfsJFJ+HpLjw5NgJd6c4sKGdkhn+cZdDvHqwB003BgUWR3DaJAzDYO2B7hGDCwD79GmUfP1rRJ55ltjLL5Pt6MDQdWzV1SglJQPZDEFRUMrLyTY3E1u7lpz+VSmLyecd55ZR4nfw2I42DnREUXWDmYVerl1YwtXzi0b09ADQ4lmyLVEM1UDKdaAUu6xym1MExWZn+pJlTF8yORMkC4sTzfEl/A888ABf+cpXRnx+JpPhv/7rv/jEJz7B4cOHR92bfDy9vb1873vf42tf+xrNzc187GMfQ1VV/vM//5O5c+eO+TxOq56LX/ziF9x+++18+tOfRhRF1q5de7KHdcahT7+cHnExestW7ETxVZYilS81JWkntGMNateAzXs0sDgWu9f012jeaAUXU8zhw4cHLjbiMY3GoiiSk5NDMBjk4MGDnH/++SdxlOZ3/khWJZNKomsq8jHO5LpugG4gSgKC6ETNREgnunH5TS8Jp0ehpyVGIpzBHRhfmWRPLD1EfvVYbLJITyw94vYjKCUl5N3zPnLvei+d3/428XXrsJUfdfQ2MNAcGQzBALtCYuNGK7iYQgRB4KIZ+ayoySOczKLpBjkuG+KbBBWGqhN7vY3krh70pGruRxFRStz4VlVOWDDAwsLCorm5GZ/vaG/rW5X4P/DAA7jdbj7wgQ/wxS9+cdS9ycdzxx13oCgKgiBw//33k9dvxvv+97+f1157bczncdoEFwA33ngjtbW1bNmyheXLlw+cvMXYSKsa25v6CCWy+Jwy51bm4FAk2mvD7FjTTF9HHE31Ikp+/AVOFqyMUjl/gu+1lgUtY2ZChkMQzKAjm5zYcSzektbWVoBBgcURBEFAlmUaGhpOenBRUVHBwYMH0bShyj2GYZq32dIaCDCcy5soCWQzBmpWH/cYCrx2MtrIr0+r+pC6/TdDkCRQtYHyQgODeHEP0coOMt44CCDNA19QpkTPIopnlwHliUYQBAKut144MQyDyNpmkju6EewSUo7DdIPPaGQaI4QeryX37TOR/Favn4WFxfjx+XyDgos3Y82aNfz4xz9mw4YNKIoypt7k41m3bh179+5FVVXWrVtHZ2cnPT09zJw5Pj+aMQcXkUiEr3zlK9x0001ceumlXHvttVRXV/Od73wHj2fqZd6Ki4u57rrrpvw4ZyqvHOzm1+vrae1LoukGoiBQ4ndw/fQCxDd6SSc03H4bsk1Ey+r0dSXY8M86gIkFGLId/GXQtR9cw/TJ6P0TSN/Zbbp3ItA07U3LOARBQNfHPyGfLKZPn86OHTvo7u7G43YhShK6piJKChkDFEHAlspiCKCpSSTZid111Owvk1Sxu5QJ+a1cOquAF/d1EUurQ1ShEhkVURDetCRqOGw104m/9hqGYRCuaaFvRjOGYCBlFAQDst4EobIm6ur/H9OnfQxRPK3WgM5I1J4kqX29iE4Z0XU04BPsMoIiofWmSOzqwXvx8H4mFhYWFpNJfX09t99+Oz/+8Y+ZN88U2plIb3JhYSEbN24knU6zYMECbDYbu3btoqhofOIWY5Zwuffee9m6dSulpeYk8NOf/jS1tbXcf//94xqAxYnjtdoevvvsAVpCSQq9dqrz3BT7HHRG0vxobS07ogn8BQ4Uu2SuYNsk/PlO1KzGrpdb0d5kBfctEQSYc72pEJWJD95mGBBpA3cB1Fw+sZM809FUszdl/Q/gle/Crr9AIjimXRQWFg4Y0h2PYRhks1mKi0+eKZphGHR3d9PY2MjcuXPJz88nkUyBw0XagLShYxOhzC7gkEXUdBpdS+HJm42kmI1salYnm9apXpSHYh9/X9bSqhxWzsqnO5amK5omq+lkNZ3uaJrOSJoLa/LG3PzrWbkS0e8jmWkmPK0VQROwxZ1IWRkhYSDHHdg8JfT0vEgo9Pq4x24xeaTrwhgZHcE5NNATRAHBJpLaH8TQh0mhWVhYWEwiyWSS66+/nptuuolbbrmFWCxGLBbjkksuGehNBsbUm/zNb36TO++8k/vuu4/PfOYzvPHGG9xyyy38x3/8x7jGOOYlsWeeeYbt27dTVVUFwKpVq6iurua8884b1wAsTgy6bvCHjU0kMiqVuUcbEG2ySJHLxoGuBFuVLOdx3IfC0Chz1yJ3NxN5dQ85Sy4Bf/lwh3hr5lwPLW9A7UtmeZTdD7oKyZDZc7Hi/uGzGhYmsS54/gFo326WmNGffdj8K1j5qVEHZkdUJcLhMH7/4GbnWCyG3W5n9jFSqHoqRXz9emIvrUXt7UXOy8N96Uo8F1886Z4MoVCIdevW0dbWRiaTMXsnnE6qq6vxuFw0bN9MvL0VezaNpsiIUhqDLLK9GrtnKelElmxaQ80aFE33MXfFxDJhsiTyyatmU+J38OyeTjoipryu36lw8cxipuW7+ce2Vor8Di6Yljus6/Px2CoqyHvf+6h94yE0kihRJwZZDE0FQUQpLUHxF5FMNtHT8yJ5eZdM6BwmkxEdkM9wjJSZWR3p3AVZxMhooBnwJn0bFhYWFhPlueeeY+/evezdu3dIpmK8vcm33347N9xwA7Is43A4CIVCbNu2bdBcYCyMObgoKChg9+7dA8EFmJq7Vv/Dqc2hrhj13XHyPfYhN0hdM/AgENI1mjMZpvU3BHnUBmYkHsGjNoCewbXFBocehhlXwkUfHd5s781QHLD6a1D8V9j3mDlZFkSoWQUL3wkVJ7fG/5RGU02Z4OY3wF9qOqaDWU4WaYWXHgR3IRQveMtd+f1+Vq5cydq1awkGgwP1mJlMBpvNxgUXXEBJielloEWjdH7nP0lu3QqAYLeTaWoisW0rsbVrKfrsZ5G8k+NNEo1Gefrpp+nt7cXtduPxeEwTrESC+vp6Fi5cyB0f/igte3dTv30L8b4QnkAOBdULifXl0nowiqYZuPx2pp9TwMzzirANs9I8VhyKxL9cPJ1bz6vgcFeMdFbn1UPdrD/cw7N7Oga+T+UBJx+8rIYLRpHJ8F1zDXb/s0ihVxHigAFSIAe5oAApJ4CAgCQ6SSSbJjz+iWJks8RefZXoCy+QaWxCdDpxX3wxvquuRCk9O8oYRc9RU7bhAgw9oyHnOEC2AgsLC4up5aabbhq28gCgurp63L3Jx7Y25OTkvGUT+Jsx5jvvV7/6VW677Tbe+973Mn36dBoaGvjd7343KHqyOPWIpVWymo5dHloJJ8oCiigQ1XXS/R9Yh9bFvNgPcWodJIU8MoIdh98NRGH3X83G6yu/OnZPCsUBS+6ARbeZGQtJBuf4P8BnDa1boG272ZOiHBPUiRL4KyBYa0r9jiK4ADN74ff72b9/P01NTei6zowZM5gzZw5lZUfrxkO//wPJTZuQi4sRHUedmPVUisSWrQQfeYSCD394Uk5x37599Pb2DlKxEgQBj8dDIpFg//79zJ8/n+rF51K9+Nwhr1ezGmpGx+aQEMfoBD4afA6Fcytz+OGaQzy7pwOfQ6Ey14UoCGRUnZa+JN9+Zj/fuHkh80rfuiHPXlyNKOzGWVY9YPp1LIaRRZZPrlO0kcnQ9YMfEFv7MhgGosuFmkjQ9+ijZnD5uc+ekoZ/k41jRoDYhnb0aBbpuB4eQ9VBM3DOzz8rszoWFhanFuPpTX7mmWe44IILJhRQHMuY78C33347zz33HLqu89JLL6GqKs899xy33XbbpAzIYmoo8NhxKCKJzFDlHdkmYdglJB08/TKxxem1OPUO4mIpac2OYpewORVw5YE7H+pegs494x+QJIOnwAosRkvbVrMUarhskSCA3QeN68wMxygpLCxk5cqV3Hnnndx1111cccUVlJWVYWgamZYWktu3E127FtHrHRRYAIgOB5LPR3zdetSenomeHYZhcODAARRFGVbFyul0kslkaGhoGHEfsiLhcCtTElgcoTmY4IW9nfgcCrluG2L/ZFKRocgdo7OvjZ89+1N27LyX1tb/JZ0Z+b3JybkAUZTR9dQwgYWKbmTIy52gt8wEiTz7HLGX1iIFAtgqKpDz8lCKilAqK1E7O+n+wQ8wMpmTOsYTgeS347mgBHQDNZhCT2sYWR0tmkHrS6OUe3EusLL3ZxPR3h6a9+6idf9eMsnEyR6OhcWEuO+++9i8efOk7W9cNQMrVqxgxYoVkzYIi6mnItfJovIAr9X24HHIA5MiAAyDlE2gVFNwhbKkfQIF6dfJ6nayuinp6ct3Hl2Vs/tMl+3G10a9Um4xQbTsm28XJbNZ3hh/071hGMTWrCH8+BNkm5rQ4nHUri6U0hKMnADCce7sks9Htr2dTH09cn7+uI8LpoJVJpNBloe/JB357KVSqQkdZ6JsaQwRS5t9S0cwDJ144jDZbAiX5GBfdz7ByOskEr+ku+cFZs38Mi5X1ZB9BfxL8fnOoa9vE3ZbAZLkMQ0DtQSpdCcu1zTy80+ewIGhaUSffRZBFJGOUwIURBG5uJhMYxOJrdtwL7/gJI3yxOFaWojokolv7UQLpjB0EBwSzoX5eM4vRnRYql5nA4lwH5uf+DvNe3aRSSURBAGXP8DsCy9mwaqrkEa4hllYnMp85CMf4fvf/z6rVq0a8T48Fsa8h9dff53Pfe5zNDc3D6n5qqurm/CALKYGQRC456Jq6nriNPQmyHUpOBSJtKrTG89QlOPkI1dVk90bpqcxhKAmUVGwu2S8eQ4cx8gvmqVQAmTjIx7PYpLJnWa+75pqZn2OJxWFsnMnZHbY9+c/E/rDHzEMA8nvN9OamkamtQ09mcI+Y8bgFXbDMHvKh8k0jBVJknC5XPT19Q27/ci1xuUaY5/PJJPKaggCg4LzdLqDbCaIKNqxyQppTUKQy3A6EiQSjdTX/4B58/4T4TjzSFG0MXPGZ6ir/wHhvi1ksr2AgCgo+HwLmT7tY9hsJ281XAuHyXZ2Io6guS7a7WDoZJoaz4rgQhAEnPPycMzORQ0mQTOQAnYrqDiLSCfirPnNz+iqr8Xh8eErKMTQdZLRCFufeoxkNMoFtxl0TKoAAMphSURBVLzLKo+zOO3Iy8sjFAqxdOlS7rvvPtzuoyW5d91115j3N+ar4p133smKFSv4whe+MCpjDosTh5rRaDkQItSRQBAgr9xD6YwAUn+fxYxCL1+/aQH/+0YTmxuDBOMZbLLIypn5vPv8SuaW+DCWFBPpSWJ7YhpK9DBSvgeB4y6UR1bHPePTP7YYB9MuBW8pRFogUDW41yUVNhVq5t047t1nmpro+8tfEWw2lP4shOhwkHU6IZtF6+tD6+1FLjjqI6H19SHn5GAfp8nOsQiCwJw5c1i3bh2qqg5ZOYnH4zgcDmpqaiZ8rIlQ5HcAAllNR5FEDEMnnTGFCQRBIpGV8TvSeGwZBEHCbi8kFjtANLoHn2/hkP0pSg6zZn6ZeOIwseheDEPD5ZqOz7doSDByohEkyQwchzExhP6AzzAQ5NEb/emJBNGXXiL64hrUzk6knADeVavwXH458iTV+k41giSgFJzcIPesxDDMUtxEj6kuWLxoZFPWKeLwpg10NdTjKyg6mqEQRTw5uaRiUQ698Rozz19BXnnFCR2XhcVE+c1vfoPdbsdut/PnP/954HFBEE5McBEMBvnGN74xSC3K4uTT2xrj9X/UEulOciShJIgCucUuLnz7DPwFpmTojEIPX7p+Hl2R1IBDd4n/qJyoIAj4C1yw9BZ45TuQTYFynNxotBOcAcuT4kTi8MFln4UXv2I2b9s8ZilUOmZOAOfdDDOvGvfu4+vXo0ejKMd8rwVZRi4oINvaimEYqD09A8GFFouhJxL4brwBaZRuom/F3Llzqauro62tDbvdjsPhQNd1Egmznvn888/H7/dPyrHGywXTcin1O2gPp6jIcaIbaXQ9i4BEOqWSThssKz6IpKdBsiGJLjJGD4lkw7DBBfQ3rbtn4nFPPEibTESfD8e8eSQ2bkQKBIZs12MxBIcDx4L5o9qfFo3S+a1vkdy2HUQR0ekk29RM7y9+aTaHf/GLKOM0bLI4w2ndChv+B7r3gZoC0QY5VXDe+2Hm6hM2jNotG5FkadjSJ7vbQ7KjncZd28+Y4CKTTNDX2QFATnEpynG9dxZnDi+99NKk7m/MwcUnPvEJvvrVr/Lzn/98UuqyLCZOIpJh/V8OEQ2m8ebaBzIValanpy3G+r8cYvU987Adk74v9Dko9L3JhWLOddCwDhrXm8GFw2/KniZ6TLft8/8NvJNjtJaKm94EDo+CYhu/4dkZT9WFcOOPYN/jpleIloHK5TD3BjPQE8f/3mW7u0EQhqTzldJSjEwGtasLra+PTEsLaCqCzY7niivIefe7J3pWAzgcDq655ho2bdrE4cOHicViCIJATk4OixYtGnAhPZm4bDL3XVbDd589QENvAp/DIJUSSKYkMprMTHsdF6ReJLVLRykpQSouBAwE4fS7VgqCgP/660jt3Em2owO5sHCgLE5PJFB7e/FcfPGoM1ehR/9EcstW5JISs6SqH0NVSR06RM/Pfk7xl75olZRYDKZ9Jzz7eYj3gLcIlBJQ0xCsgzVfNzPps8a/sDJaDMMgEQ4jK/Zhtw/0hcWjUz6WqSabSbPrxec4vOl1ktEIAG5/gFnLL2b+ZVcgjSFbaXH6kclkUBQFwzCGFVgZDWO+49XV1fHUU09RXV3N8uXL8R2zavmrX/1qXIOwmBgNu3qIBtP48h2Ixxg4yYqIL89BX2eC5n1BapYUjn6nNhfqFV8jtf63KA3PoiTCiLIEpUtMGdlJyFr0tsY4sLGDtkN96JqBzSFRtTCPOctLcHqtkrthyZ8Jl3wCLv64WSYwCf0OAJLbDcPoZguiiG3atP4nSbgvuAApNxfPRStwLFxols5MInbdYEk8TvnBQ0SiEZTcXMpnzMA3Y8YpM+lcUZPP125S+Pu2VjbtaiCTFPHao5yfc5iLA7twiSJGRiXT0owoJpFyfPi8p6fwgWvpUvL+9V8JPvII2ebmgc+IYLPhvuB8Cj7876P6f9EiEWJr1yJ6PIMCC+jPkOXmkdqxg2xjI7bq6qk4FYvTEcMwTULj3ZBbc7QcVHFCTjWEGmHTwzD9UnPRawoxG7f9hNpaRxiq+d1weibH9+dkoakq6/73t9Rv34xid+Dy+8GAZDTClif/QaSni4vedecQhTuL05toNMonP/lJ/vnPf9Lb28uWLVu45pprePzxx1m6dOmY9zfm4KK6upoPfehDYz6QxdTReiCEKAmDAosjSJKIAbQfDo86uNB1g0NvdHBwUyfxvvMR9cX4HGFKZucz56rzsLsmvmrRWR9h/V8PkYxksLsVFLtIJqWy99U22g+HOf/6aXhyHTjc1grJsAjC2D1G3gTXsmWEH3scLRYbogyEpiGIIrn3vI+cd71r0o55PGooROc3HyS1Zw9IEgGbDb2xid5t20muW0fhp/8DyXNyfR+OsKDMz/wiN/ufeZgu8SDaynZs6EgpGyAg2G1oegI13klJ1WqcztO3TML3tqtxLllCfP16su3tiA47ziXn4ly0EGGU2etsayt6NIo0gqGT6PWSDQbJWMGFxbH0NUH7dnAXDH+98xZDX7NZNlV14ZQPZ8Z5y9nw9z+jZbNIyuB7UyoWxeZwULnwnCkfx1TSsnc3jbu24Q7kYHMcLYn25OaRTsSp3fIG089dRumsuSdxlBaTzT333EMkEuGRRx7htttuIxAI8PGPf5x///d/Z8OGDWPe35iDiwceeGDMB7GYWtSsPmxgcQRRMJ8zGgzDYMeLTex7rR1RFHB6FATRRjThpGerSk/4EJfcNmtQidVY0TWdrc82koxl8RcelbiVFJFsRqN5b5CuxgieHAclNX5mXVBMQcXIq0GRVJbNDUGiKZVct43zqnJxWuVVY8KxYAGu5RcQf+UVUFVTIUgQMBIJ1O5ubNOm4b3iiikdQ+8vfkly926U0lLEY8Qi9FSK+MY3CD36KPkfeP+UjmEspGtrsbc0UOXPJ1YrEp7eQtaTHNguaAKOWjvFFZecxFFOHFXT2ZlSaJl+PraZIudUBsjzO9/6hcdypDlcH+E6pOvmdqvU1uJYUmFQM+AIDL9dtoOhQqrvhAyn5rwLqN+xhc7aQzjcXuxuN3q/WpSWzTJv5eXklpafkLFMFfXbNmFo+qDA4gh2l5tEOEz99i1WcHGG8cILL7Br1y4qKioQRRFBELj77rv52te+Nq79WVfyM4DcUjfBtuFlYQ3DQNcMcopGp27S15ng0KYubA4Zp+foyozLZ8PmlOmsj9C4q5eZy8bfeNlZH6GvK4HHbxsILHRNJ9gWJ51QMTDIpDR03aBhZw8ddWEuvKWG0pmD1WQMw+Dv21r506ZmemNpQAABSvwO/uWiaayaM4YysLMQwzBIbttGdM0a0gcPmd4FZWVoPT1okTAYIDjsOM45h4J//xDyCKvOk0G2rY3EG28gBwKDAgvoN+zzeIitfYmcd96KdJKbuo9gpFIYqoooK/jry3F25ZIo7kV1pBBVGUd3AGl7GOHCU6Ocazwc6IjygzWHqO2KkdXMwMDjkLliThH/tnI6DmV0Qby9uhqluJhsWxuic+ikRevrQwr4cZwCfTUWpxCuXDOAyCaHL3vKpkCUwZl7QoZjd7lZdfe/sfXpx2jauZ1obzcIIu5ADnNWrGT+pVecMuWb4yUa7B2SlTkWSZaJBYMncEQWJ4I5c+bwm9/8hi996UsI/f2Xr7zyCvPnj06w43is4OIMoHphPg07e0hGM0N6FRLhDDaXQtUo3WOb94XIplT8hUMnALIiIghQv6N7QsFFrC+NoRvIx2QXoqE06YSKrJh1nJpmYLNLuLwKkZ4UW55ppLDah3zMZObxne387OU6ZFGgLOBElkQyqk5nJMV/P38QuyKyomZi5m6nHcmQudrn8L+p+7lhGAR//3vCf/sbRjqN6HBi6DpGKoUYCOC7/nps5eXYqqqwz5kz5TfMdH09eiyGUjF8+ZDk86H2dJNpasY2z0NTU9NA07fb7WbGjBlUVVUhTXIPyJshFxcjupzo8bjpYB13Yas9GsRrsRi6w4lSPDnCByea5mCCrz2xh45wiiKvA6dNQjcM+hJZ/rGtlVRW49NXzx7VZ0Ow2fDdcD09//NT1GAQKScHQRDMxY9oFD0WI3DrO04bOVqLE4S/HMrPg8Mvmop5wnE+O7EOs/eidMkJG5LL5+fi295L7KrrCHd2IMoS+eVVE1JSigV7Cba1IAgCeRVVuHwnbwHF6fPT09w44nZd03B6J0ch0OLU4Yc//CHXXnstP/3pT4lGo7zrXe+iqamJxx57bFz7G1Vwcfnll/PEE0/gcrlYtWrViDeTNWvWjGsQFhOjsMrLvItK2bOujb7uJHaHhAFkkiqKXeacKyoIjDJzkYxmzATACP/Hsk0iHs5MaLxHFKF03UAUBXTdIBnJIIgCgiiga4bZUiCa0bMnx060N0X7oTAV88wVqkRG5c+bmpFEKPYfvajbZJHygJOmYII/b2rmwul5p/1K0qjorYXtf4T6l80yAsUO1ZfCkjtMA77jSGzcSPgvf0VwOFCKjk5+DV0n29pCYsMGcr7//05Yj4MgimZN9TBN5XCkWVJANXReeeEFamtr0XUdSZLQNI1Dhw5RXV3N6tWrsduntrHzCEpREa7zziP64hpEj2dQ/4Ghaag9PbiWnott+vQTMp7J5qld7bT1pajOcw2YBoqCQK7bhiQKvHKwm1uWlDGzaHQNrL5rrkHt7SXy2ONkG49OXgSnE+9VV5Jz551Tch4Wpznn/Qt07TXVodyFYHOZcrSxLlOSe/kHJ2QeOl48Obl4ciaWMUnFYmx58h807NxGJhEHQcDh8VBz7vksueYGFPuJl36tXnwuzXt2omYyyMdlkbPpFIIoUrXonBM+LoupZdmyZRw+fJgnnniC1tZWysvLue6668Yt/z6q4OLuu+8eMMx73/veN64DWUwdgiCw4NIyckrc1G7toqclhoCZ0ag5t5Di6aP/cNhd5kfCMIxhJ+WaquPJndgFr3i6H7tbIRnN4vbb0DUdXTMG+kY0VcfhVpD6sxhHpHVjfamBfexqCdMZSVHiHzoWQRDI89g51BWjridOTYFnyHPOKLoPwlOfgnCLma1w+Mwygj1/g7YtcM1/Qv6MQS+JPPc8RjaLUlIy6HFBFFFKSsm2tRF//TV8V155Qk7BPmcOkt9vGvMNU36l9fUh5eeztbubvXv34na7cbvdA5/RTCZDXV0dGzduZOXKlVMyxmxbG/ENG9HCYSS/H/fyC8i9804y9Q2k6+uQ3B4EpxMjnUaPRlBKy8h7/wdOy+BW0w3WHujCbZMGuZEfweeQCcYzbKwPjjq4EESRvLvuwrtyJbHXXkMLBpG8Xlznn39CsmMWpymFc81r2Mafmc3dyaBpnle80Aw8qi8+2SMcF9l0irW/+wVtB/bh8HjwFRaBYZCKRdn98gvEw31ceue/IJ7AbCxA1aJzOLxpNm0H9uLw+HB43GCYgVAqHqNywSLK546vVMbi1KOjo4OXX36ZTCbDxRdfzB133DEp+x11cDHc7xanDoIgUD47h/LZOWiajgCI0til4spm53BgYweZpDpEFUpTdTRVp3rhxEqNHB6FWcuK2PVyC4lIBptDAsEMaNSMgSgJeHIdA77ghmFgwKAyqkRGQ9MNlBHO0SaJqJpBMjO8u/AZg2HA6z8yA4u8mqNlAza3WYccqoUNP4Hr/mtAbcXQddL79yO6h89KCLIMhkGmvuEEnQTIOTl4Vq0i/Pe/o9ntA4pVhmGgRyKkVJXGpeey/Y030DSNVCpFJBLB5/Phdrux2Ww4HA4OHTrE0qVLiXV10HH4IJqaxV9YTOX8RUfLFgwDuvZBxy5TIz9/lllWMYK0oqHrhP74v4Qfeww9GgUBMCD06KP4b7yRoi9/icgTTxJ/5RX0ZLK/BOhG/DfdiK389GzuzGo6aVXHJg//npg1uYzr+2WrribXUoSyGAtF8+CG75vZi3i/Q3fBnEmT4j4ZNOzcRvuhA3jz8gdlCFz+ALLNTtOu7bQd2k/5nBM7kVdsdi597/vZ/Nhfadqzc8BEz+HyMOeilZx3/S2Wz8UZwnPPPcc73vEOpk2bhizL3Hffffz4xz+elCSC1XNxBiKNI6g4Qn65h6oFedRu7UZTDRweBUGAdFIlFc2SV+Zh2uKJ9zHMv6QUwzA4tKmTWF8GQRDIpjUUu0Sg0IXDdfSjmYpnsTlkSmqOZmAKvHbsikgio+KyDf0YxzMqDptIoffElMicNHoPm6t5nqLB9chg3njdhdC2FUL1kNtfniMI5rYRSpAADAyECXyOxkPuXe9F6+sjvn4dWm8PiBKGrqG63GxfcSEd6TSapiHLMoIgkMlk6OnpQdM0/H4/TqeTUDDIM4/8gnhjLWom0x9PCXjzC7jwHbdTVl4Iax+C5jcgmzDfC8kGhfNg1eeHLSGLPP44oUcfRXQ6USoqEEQRQ9fRQiFCjz6K5PWQ/68fIPfOO9CiUSS3e8TA7XTBLovkeew0BxPkDFNRqelmwF9wpn+/LE4dBMFcQMmrOdkjmRTqt20BgSGlRwA2p5N4X4jGndtPeHABplfHJe95H5GeboItTSAI5FdWT7gMzOLU4j/+4z/47//+bz7wgQ8A8Pzzz/Oud73LCi4sJk5XJMW6wz0E4xk8dpnl0/NYdu007G6F+u3dRHtTGIaB4pCpXJDHuVdXTYr3hCiJLFpVwYylRbQd6iPYFuPwli4MwOY0MxSGYZCKZ8kkNWYvL8aTc7QEal6JjxmFHva2RajMHVy6oekGoUSGK+cVv7kL+ZlAtMMsgXKP0GBvc0MiCNHOgeBCEASzV+DZZzHyhvak6Ok0giRNuXKPqqo0NTXR3t6OYRjk5eUx7cP/ju/qq4i/vgEtFEIqyKe2pITeujo8djupVGpAJk8URVRVJRwO43a7EQWBRCRMZ18neR43npxcBEFAU1WiPd2s/+MvuWl2N/beXeb75S02JyyZuBmAPfNZuOkn4D5alqWnUoQfe9w0ejumXEsQReS8PLIdHYQfexzv1VcjOp3DKiGdjgiCwDULivnxS4dJZbUhqlBd0TQ5LhsXz5x6wYTeWJpXD/XQGUnhskmcV53LnGKvVUZ1NqJr5vVOcYI4heVCPYfg4DNmdlOQoHI5zLravGZMEolIH/KbZABESSIZCU/a8caDL78AX37BSR2DxdSxd+9ebrjhhoG/r7zySlKpFO3t7ZQcVzI9VsYcXOzbt4/q6mqcZ8hN9GzFMAz+sqWFP77RRCSZ7X8M/rixiasXFPFvq2qYe2EJ3c1RDM3AX+jCXzC6/3M1o5GMZZFtIk7PmzfauXw2ZiwthKWFlM3KYcuzjcSCqYHx2Jwys5cXc87qykGvE0WBe1fW8PUn9tLQmyDHpWCXRZIZjXAyS3W+m/curxrHO3OaYXOZUoxaBsRhAiktY9YnK4P/73xXX0V8/XrUjg7koqIBt1Ujk0Ftb8c+Zw6ucbhyjpZQKMQLL7xAV1cXer/3gSAI+P1+Vq1aRfl995rjMQye+9//RZZlnE4noigOZC8AJEkim82SSCTQsxn0dAq/x4PtmOuTJMv4C4tw9WxDazwIpTMHvx82t6k401sLh56Fc94zsCl98CDZri7k/OEn0VJuLtmuLtIHDuBcvHiS36WTy9sWFLOxLsimhiBOm4TPIaPqBsF4Focics9F1eR7pjZz8fSudn65rp5QwhSRMAx4dFMzF8/I56OrZw6btbQ4A4l1w95/wIGnIB01y6JmXwvzbgbPJE9+9z0O674PqRDIDvND1/IG7PoLrP4KlE/OddGTkzui2zeYqkzugKWeZjF1aJqGyzU4Ne10OlFVdcL7HvOV+YorruCPf/wjl1122YQPbnHyeH5vJ79cV48sClTmmmowhmEQSmT5+9Y23DaZ9100jYo5o0+DpuJZDmzooH5nD5mkiigKFE3zMXt5MYVVby1dVzY7h6JpPtoO9RHrS6PYJIpr/HhHaCBfUObn6zcv4P82N7OpIUQkpeKQJW46p4x3LqugLHCaB8C6Du3boG0baKo5AZ52iTkZPkLRQghUmk62gcqh+4h1mxmLogWDHnbMnUv+Bz9I7y8eJtvUBKJoNvGLIvZZMyn81KcQhknXTwbZbJbnn3+ejo4O/H4/Sr+muqZpRCIRnnzySSorKwkGg+i6Tnd3Ny6XC0mS8Hg8hMNhdF0fyGCA2dCdTsRxZJK4cocKGAiCQKkjRDYdJ52VycbSANgcEopTQhBlDFEmseMxDgvzkSSJ8vJybJkMvaKDXa4qYpIdn5HlXLWHXMOc7KYlGz2SC1skRfkIIginKy6bzJdumMdfNjfz/N5O+pJZJEHgnAo/bz+3nItmTG3WYkNdLz9eexhdN6jIcSGJ5jUqmlZ5fm8nNlnkk1fNntIxWJwCRNpMwYruA6C4zIWBeA9s/CnUvQTXfhd8pZNzrM498Op/g56B3BlHXcENHUKN8OLX4J2/Nv03Jsi0c86jec8usuk0ynEKd+lEAsmmUL343Dfdh65rdBw+SOOu7STCYdyBHKoWnUPx9JkDC0YWFiNhGAaVlZWD7lt9fX0sXLgQ8ZjPT3AcviZjDi7uvvtufv3rX1vBxWmMqun8bWsrum5QdMwEXOiXmVR1gyd3tXPzkjICrqMTTMMwCLbFScWz2F0yuaWeAYWndCLLq386SFdDFMUuojgkdM2gaW8vnQ0Rlt9cQ/nst16FkW0SlfNHb9Y2q8jLF66bRzCeIZrKEnDZ8DvPgGazeK95I2vdDGq6/yYnmLrvqz5var+DKcF47l3w0kMQbjV7LyQZtCzEOs2sxbnvNR87Du/lq3DMn0fslVfI1Dcg2Gw4Fy/CvXz5lJb31NfX09XVRSAQGMhAgJmFkCSJrq4uwuEwfr8fXddJJBIkk0lEUSQQCKCqKolEYmB1Rdd1MpkMeR43RmcTMLw6mkIWLQs9LTHAXJAURAG7S8YVkEhG40TDdbwafdV8vmKjRShh87RriYs2BEnEAP5qVHN5phUDkfVCHtFqGefmBIt79/D2c8tYUnnmrDZ67OYiw23LKumOprHJIkU++5QHUUcMMpMZjapc18DxBEHA51DQdINXDnbzrvMqqMgdncy2xWnK6z+GrgPm4sqR65gzx1xw6TpgClZc9Y3JOdb+JyEdhtyao4EFmP1sgQpzEefwi7DonRM+VNWiJVRs20TT7p3YnC4cHg8YBslYlGw6zawLLqS4ZuaIr1czGdb/+fc07NiKls0iSiK6pnNwwzqmn7uMC2+93Wq8tnhTXnrppSnb95iDi9WrV/OlL32J6667jg9/+MO4j2lcnCoJSIvJpaE3TlMoQd4IJUt5bhvNoQS7WsNcMtNMOXfUh9n1UgvB9jhaVkeSRQJFLhZeVkbpzBwObOykqzGKN88+IB0LprRtpDfN1mcbKZrmG/C4mGxy3TZy3Sde63xK0FR44cvQ+Dr4SkBxmzc6LWOqQj3/Zbjxh0cbG2dfa+q+b/41hJsBA90wSIg+WkuuJtyXS0V7O8XFxUMmhUpRETnvnPiNciy0tLRgGMagwAIgnU4TCoUGxuj1mhKnmqYRCoXo7e3FbrdTUFBAIpEgkUgM9GCsWrUKZyrGhtq96Jo2RL5R1wx6ojLFHrOX/YiSmq4bJGIpIskYARIkPNPIzc3FMAze6DJY15vF5XJREupCdjrRBYEewcGvHLOxGxq56ShejwPBbue1wz3sbOnjk1fNZuWsM6tO2WmTqMw7cZP4nliG/e0Rcly2YQMZv1OhKZhgW3OfFVycyfQ1Q+NrZh/U8Qskkmw+3rDefF5geAPOMdGy+ej19nhEGTCgczcw8WumrCisvPNf2P7ck9Rt2UQs1IuAgNPnZ+Gqq1h4xVVvmn3Y8dyT1G55A5fPj/2Y0pZ0Is6hja/hzsllydXXT3icFmcul1566ZTte8zBxZGu8vb2dj70oQ8NPC4IAnV1dZM3MosJYRgGtd0xwklzNX96/lFPgLSqo+sGkjj86qPY72WW1cxa+M6GCOv/7xCpuIrLpyD7bGhZnZ7WGOv/Wsvym6ZRv6Mb2SYOCizA/Fx4AjZiwRRth/qoGkNW4qyldQu0bgVfmdlTcQTJBjlVphzjvsfh4o+ZjwsCLHgHzLgSmjbQXr+X3bWt1KpFqG0OjNY32LptG1VVVaxatQrHBJxkJwNVVYedMMZisYFyp2Pxer0DgUQ4HCYvL2+gLlRVVcrLywkEAhTmTWfXC08TDfbiyy8YdIxoMEosWsi8QB8OMUJGMLMLoiRgGBkkPQ6SQrvvHHOMhsDeuBtZBJssYHg86LEYSBKazUZKFBF1lTwbOCvLEO0KfqdMa1+Sn71Sy9KqHNx2qx9gvGQ1Hc0wcIx4jRIQMLOwFmcwfU2m4MJwJZ9g9l70NZuLKpMRXLxVRs6Y3IydzeHk/BtvZeHlV9PX0YaAQG5ZOTbnmwfMyViUQ5s2YHM4BgUWAHaXm2w6zaGNrzF/5eVD9hXp6aZh+xaCrc1IikLJrLlULVg8IYdxC4vjGfPdr76+firGYTGJbG0K8bvXGznYGSXTr1U/t8TH3RdWs7DcT1nAicchE02p2D1DMwmxtIbTJlEWcGEYBrvWtpCKZ/EXOAcmbKJdwp/vINKTYseLzaQTKjb78FmJIwFHvC89dSd9JtHyhpmlsA1zgxFE05W2dg1c9NHBN0OHjxbXPJ5qbEBlGr5cH2J/L0Umk+Hw4cOIoshVV1315mUtRyRqp6j0JS8vz/QuOa5HIZ1OI/T3/hzrsq0oCoWFhbS3t5NMJgkGgySTSVRVRZZlurq6eOaZZwgEAsy46HIOv/QMfR3t2N1uRFEknUwQD6WRHEtpdS9geuqvOLUOBENFALLoZFA4LC6ly2mWIbQnJSJZEY+iY2gGWnExjlSKbE8PQcmJgoFqd6KWFCH2j1UQBIp8DtrCSTbU9XLF3BEUvCzekjyPjVyXjZ5YGs8wQVoqqyGJAuXD6eRanDnIdjPVqKvDq0Ppmvm4NElZ67LzoPfR/prJ465/ugoYpnnfJOP0eHHOGH3/UG9TI8loZEQlJ6fHS6wvRG9LMyUzj+730KbX2fTYX0lFo6aktmFwePNG9pSWc9ld78dfOHlqWBZnN2Pq+Ons7KS5uXmqxmIxCWxpDPKNJ/ayo6UPj12m1O/AbZPZ2hjiq0/sYVdLmIDLxqWzCoikVDLq4JU/TTfojqWZV+pjVpGHvs4Eva0xXL6h5QmCIODy24j0pkyDPW143wRzIsmUlUSdcahp4E0m9qJsPmcYn4rdu3eTTqfx+/0DGQBBELDb7bjdbhoaGujp6Rm6T8OAupfhyU/Br6+BR26Alx40GxwnmZkzZ+JyuYhEIhjHnIMgCOi6bma7PINd1W02Gx6Ph+nTp1NRUYEsy+Tm5lJWVkZeXh5er5e+vj52NTRxzttvZ8ay5YiiZErclleSX305geJVJOQqDEQkI41EBpEMMmZzdh+5GIL5GVUN0AGp3yfDEEVsZWXI8xagu9woLieCLGMcV9plmjoKdEWtQHoi2GWJty0oIa3qQ4z6dMOgI5yiOt/NuZWBkzNAixND0XzwFJsN3MMR7wFvifm8yWDu9eDwQ6R18PVV18wsir8cZlwxOceaAIbRf98e6TYhmOUHA88DOusOs/Hvf0ZNpwkUlxAoKianuARvXj69LU288offoKnZqR+8xVnBqIKL2tpali5dSklJCVVVVcyYMYNNmzZN9dgsxoiuG/xmfQN9ySzVuS48dhlZEvE6ZKrzXARjGR55vQHDMLjjgioWlvlp7UvS1pekL5GhM5KiMRinOs/Fh1fNQBAEUnEVTTWQleEDA1kRMXTIKXaRimcHTRaPkIplsTllSmYM32hrcRxHSgD0EdyP0zHImzHEnTaVStHS0oLT6Rw2M2G328lkMrS0tAzeYBiw4afw7Oeh/mUza5KJwe6/wmMfgUMvTMZZDeDz+bj44ouRJIlgMEgsFiMej6PrOoZh4PP5hkhdH5GrrayspLu7G6/Xi8/nGzhPWZYJBAIkk0mau3tZecc9vPNL3+TWL3yd6z/6GSoWXICshZmZ+A2GIBCUFxFSFhKS59NKDUnDyWLtdXJSjQAEFB2baJDqvzfLsvn5l0QBQRBQNR1JFHAcVwao92dknCN8XyxGz81LSlk+PY/OaJqWUIJQIkNXJEVDb4Iiv4OPXjEL+QQbPVqcYBQnLH436FmIdZmqTWD+G+syH1902xCp7XFTOBdWftrcX7DWDChCDeaPtxRWP2A2k59kckrLsTtdpGPxYben4zHsbjeB4qMqWgc3rCeTSODJHextJMky3nwzwGjZN/mLSRZnJ6Mqi/rgBz/IsmXLeP7555EkiR/84AfccccdHDx4cKrHZzEG9ndEOdwdp8g7VM1FEAQKPHb2tUeo7Y4zo9DD129ewNO72nl2Twd9ySx+p8ItS8q4blEpxX6z/tLhlpFkATWrYRtGcUjN6kiyQM2SQuJ9aSI9KTwBO5IiYuimCV46pTHnOBM8izeh5gqzOTvSCv6Kwen5VMT8e+7QRj1N09B1fUij9BEEwZwYa9pxQUvja7D99+YN1V9+9HF3oVnL/Op3zVIA7+SV+cyePRufz8fevXtpbGzEMAxqamro7OwcOI8jmRdd1wmFQgPqUqlUitzcoVKQgiDgdDppbm4m1t2NvmUL8ddfR4/FCeTPQdCaUMQgSbkMBBEdGwggCTpxVAqECBWxHYQcVeTaDSqcKgciErk2CZfLFK6QJRG/U6E9nCLXLQ8xl+tLZPHYZZZNs5xsJ4rLJvPF6+bx7J4Ont3TQWckhcchc92iUq5bVGI1cp8tLHyXed3b+SiE6oH+pkCHD5Z9ABZOsiDF7GugYA4cfNY00ZNkqLjA7GmbbE+NceLJyaVq8RIOvPYKisMxyOVbzaRJJxLMu/RyXD5zQc/QdVoP7MXmdA278CQrNnRNo6uhjqqF55yo07A4gxlVcPHaa6/xi1/8YuCG/rnPfY6vf/3r9Pb2kpdnNeiOl3ha5XBXDM0wqM5zT1jtqC+RIaNqOJThja0cikgwodPXb0jldyq8+/xKbltWQTKrYZelIU3egSIXeWUeOuvDKHZp0IXJMAzi4Qx5pW5qlhbizrGz9ZlGIr1JMMzrv90pM/fCYhZfMUJDnsVQ3Hlw8cfh5W+Zq2cOv1kKlQoDAsy53rzRHYfD4cDr9RIKhYZt2j7SSB0IBAZvOPAUaOnBgQWYQYyv3Lyh1744yFxuMigpKaGkpGSg/0IURZqbm1mzZs2AatSRvoxAIMCVV15Je3v7QJA0HJIkkY7Haf3mN5EPHgJBQJBlbHv3Ujwzi+HXUA0DUTJXQHXNQMKG3eYko8XxxutIe9Pous5CW4RuJZ+U5KU3nsWp6CSzGrph4LFLyJJIOqthVyR0w3SFj6ZU3n5u2envsXKK4LRJ3LykjJvOKSWt6iiSOKIQhcUZiijCBf8Gc66F+lcgGTKzB9NWDr1mTRa502D5fVOz70li6bU3E+nppuPwAUBAttlQM+a9vXzeApZcfdR52WBoj9twGLolkGAxOYwquEgmk+TkHE0FyrKM2+0mkUhYwcU4yGo6f97czJM72+mNZTAw8NoVLptTwN0rqvE5xqdN7XMq2CSRdFbHOUx/Q7q/uft4HwhBEEZ0uhUEgYWXlRPuShDuTuLy2ZBtElpWJx7JYHfILFpVjigKlM4IUHivl/baMPFQGtkmUTzdjydnal18z0hmXWWuku3+OzRvMMsAihbAvJtM6dlhskiSJDF37lzWrVtHJpPBZhvsURKJRMjJyaGq6jjn8s49oHgYFlE063p7Dk/iyQ3m2GChoqKCW2+9ldraWjo7OwEzCJkxYwYOh4NYzPSo0DQNSRr6Gc+k04idXRgHDqKUliIoRz/rHn0jihpHklR0XQEBFIeM22/D4Q2Q6kqQUhUymYz5uZ9eytU1c9kelHhxXxeJrIqvP7tXne/mf99ooimYQNfNm7fXofDOpeX8y8XTp+y9OlsRBGFIlsjiLMNfPukLHKczDo+H1f/yQRp2bqVu6yYS4T7cOblMX7KMqsVLUGxH77uiKFFYPY2mXTtw+YeWJ2uqiiCK5JZOUbBmcdYxquDCMAze8573DDjpAsTjcf71X/91kHX43/72t8kf4RmGYRj8aM1hntjZhkORKPTaEQQIJ7P8bWsrzcEEX71xwbDBwVsxp9hLdb6bQ51RKnNdQ7IMXbE0i8sDzCgcYSI5AkXVPi5+1yx2rmkm2B4nGc0iySIF5R4WXFZO6YzAwHNlRRqTq7fFm1C6xPzJpkylEtsI+uvHMH/+fNrb26mrqyMej2O329F1nXQ6jcfj4dJLLx30PQZMpRVjhP4OAIPJU2MZBW63m0WLFg27raqqikAgQF9fHzk5OYM+46qqko7FmN7ehrOgYFBgAaBJpch6G/5sB8rMeSAISHJ/YGMYuBVwLHsXty28bcANXBAEzgPuurCaREbFZZOx9fdZXDKzgC2NIdrDSRyKxNKqHIp8oyv903WVaHQXmUwPkuTC5zsHWXa/9QstLCws+lEcDmaev4KZ5694y+fOPH8FLfv3kIiEB8qlwMxWRHu78eUXUrlw8VQO1+IsYlTBxQMPPDDkscWLrQ/heNjTFuGFfZ3kHOckne+x47HLbG3sY+2BLq5ZWDLmfcuSyF0XVvOtp/fRFEpQ6HFgV0RSWY2uaBq/Q+HO5ZVvmRodjqJqH6vvmUewPU46nsXuUsgtcSNYJQpTj6SgHlxHdv1fUDvb0EU/wtxrcF9+LdJxJU6KorB69WoOHDjAvn37CIfDKIrC7NmzmT9/Pvn5+UP3P20lbPm1mR0RjmuQVdPmY0ccwU8yiqJw8cUX88ILLxAMBnE6nUiSRCaTIZPJUCDLTO/sRCgtG/LaVKYYl+JFFkKIehrRYUotG5k0xNoRXAVI864fWjYG2GQRm2wb8tiFNWPP3IbDW2ls+gWJRAOGkQVE7PYCSkveRVHRDVPufm1hYXH2UTF/EYsufxu7XnqOUEc7it2Ormlo2Sye3Dwufvdd2BxWOafF5DDu4MJifKw73EMyo1HkHVoq5FAkBAHW7B9fcAFwYU0en7lmDr97vZGGnjgZzSyFmlPi454V1SytGn9WQRAE8krHlvWYDKKZKIlsAq/Ni0s5y5o4E0Gyj96PcegVJD3LkXyWtm4tvS/8Hu+H/hPngsEyjIqisGDBAubPn4+qqoiiOGwJ0QBzrjP7LkKNphGV2H9ZUFPQ1wLF86H64tGP2TAgHTWzHcoYmvgNw2ygbNsKWhZypkH1RUOUYKqrq7n22mvZsWMHLS0tZDIZHA4HixYtYlpLK/ERjNUMQyHUM5dA7h7kcDNqRxIjHsfQdXTdQbpiFa60k/EVJY6OaHQPhw49RFYNY7cVIklOdD1LJttDQ+NPAYPi4pumcAQWFhZnI4IgcM7V11E0vYbazRvpaW5EkhUqFyxm+tLzR/TMsLAYD5aF7AmmN5Y23WVHWJ10KBKdkYlp5K+oyef86lz2tUeJpLIEXApzin2nXSNkfbieJ2qfYGPHRlRdxSk7WVm+khtqbiDfOcwK/JmGYaA9+QU4/BJqWkZQcvp1zQ0kRwKfuIPQj76A8tCvkY/piTqCIAhDS6CGI3caXPGA6WvR19Sv726YQUbJIrjyq6MLEtQM7H8c9j4G4RbT3KpiOSy81dzPm5EIwkvfhOaNZhmYIACC6Uh+2WehbOmgp5eWllJaWko8HkdVVZxOJzabjeTuPSRsNoxEAsE9tMwo1WMQ8l1JsqMLMbgfUQ6Q1XNJ9PnJ7tiBbduXKPrCF7BPn5q+ida2P5PJhnA5q48aUooKDnsJqXQ7bW1/Jj//CmT5xAfxFhYWZzaCIFA6ay6ls+ae7KFYnOFYwcUJJtdtQxvGnfgIaVUj3zvx+nZZEllYfvr6ShwIHuDbm75NV6ILn82HU3aSVJP89dBf2da1jS8u/yKFrsKTPcyppWMXxuFXyCYlBPuxE2UBTXch22M4I/uJv/IK/psmuNpdeQHc9luofclUqBIkKD0HKleAPIrPo5qBNV+HQ88CEji8Zp/Igaeg6TVY9XmouXz41+oavPBVaFwHniJTT14QzH2GGuG5L8FNP4bcaehplfShPjLtcTAMlCIX3pk5iDYziHLMn4dj3jySW7eiKApCf1O7YRhofX2mepTDT9+ORmylFyJIphytmAM2n0ampYWen/6M0m9/a9LLk1LpDqKRndiU3GH3bVMKSKXbCIe3kpe3clKPbWFhcQxH/JisEkQLiynBCi5OMCtq8nlsRxvRtDpEFSqt6mi6weVzzvBJ81ugGzq/3P1LuhPdVHorEfv7ANyKm4A9QF1fHX/a/yfuP/f+kzzSKaZ1C0YyioE8jBGrgG7YsHtjRLZvnnhwAabk7fybx/faA0/BoefAVQD2Y1bdXfnQ1wyvfg9KzwVnYOhrW7dA6ybTadd2TBAl28zMRbAO9j1Otub9hJ+uRw2mzCZzIClAfFMn/qursVV4TT+Xj36Ezm99i/TBg2BgOmlnswhOB77rriX2yqtIHs9A4HEEQZJQ8vNJHzxIev9+HHMnd3VPU+PoRhZFGj4rIYoyYKBqsUk9roWFRT/dB8zMasMrZullwRyYdyNMX2UFGhajZv7eX+G1Ta7ISbRfRvhMwbI3PcEsLPNz2exCgvEM3dE0qmYGFMF4hta+BIvKA1w2++wOLvYF91HXV0eBs2AgsDiCLMr47D42dmwkmAqepBGeIHS1fxI9/E3PMAQEAQw9e0KHNcxAYN/j5u/24ybOggD+Uoh1Qt1Lw7+++Q2zcdw2jFqSIILNjXHwBfqerEPtTSEF7Mj5TuR8J1KOAy2SIfx0PVrYLCdUiooo/cY3KPz4x3GvWAHz5pF+29uQP/UpnJdeihGPI3qGn+ALLhdGJkO2rW2878aIKEoOomhH0xPDbtf1NIIgYlPOgpI/C4sTTf0r8Nj9phlfKgJaBhrXw3NfhPXfP5rNsLCwmDBW5uIEI4oCH71iJvluG8/s6aQtnEI3TI38axaU8K+XTMdjP7v/W7oT3aS19IjN227FTTAVpDvRTa7jDJa9zalGsNshmgaGCgCIYoZ0yoZ96UlWblPTZq+G3Tv8dlE2b9zhluG3a2lGCqDM1yvo0RhaJoGUO1ihTBAFpBw7am+S5L4gnuWmEILodiOtWMFBm41Dhw6RTqcRduzAZ7NRkpvDNFUbfmVF1wFjSFZjMrDZcsnNuYjOridRZD+CcLTJ3jAMUulOnM4K/P5zJv3YFhanBdmUOem3eyc3k5AIwsvfMY1Ic2uO7ttdYJry7fo/KFk8cummhYXFmDi7Z7EnCYci8f5LpnPr0gr2d0TQDIPp+R6K/WNQ1jmDsUt2REFE1VVkcehHVNVVJEHCIZ/h71f1xQhFM1Fi28mmU4j2o+crCFlQVZKZGnIuu+zkjRHM4EGSIZscfvuRFUFpBDNFX79xk66ZTeDHk4mhMgMEaVjpY0EQEGSRdEN4ILhIp9M888wztLa2Yrfb8Xg86LpOOB6np7ycVHMLC4cZihYKIQVycCxY8BYnPT5KS28jGttDItGAouQgSS4MPUMm04useKmsfD+ieOI8RSwsTgnad8Luv0LT6+Z1wF8Oc2+AuTeOrufrrahdA9EOs8zy+KDFmWMGGPuesIILC4tJwiqLOon4XQoXTM9jRU2+FVgcw8L8heQ58kYsewomg0zzT6PSW3mCR3aCUZxI13wdsbAKxZFCUMOgxZCMPgQtRjJRjOvOr6KUjE+2eNKQZJh2KaSiw5cWZGIgO6Bi2fCvr7kc3PkQaTGbuI1jpGTTUQzDIK5cgKpm0fXhzf4EQQDt6LH37dtHa2srfr8fj8eDLMvYbDYCOTnYAgHqCvIJBYMYmrk/wzBQQyH0RALv1VcPq741GTidZcye9VUKC64GDDKZXjQ9iT9wLrNmfpHcnAun5LgWFqcsdWvhyU/A/ifNUlBRgp4DZqZhzdfMa8JECdWb/w6zWAWYmZKuvVZplIXFJDGqzMWqVatGpZyyZs2aCQ/IwsJj83D99Ov57b7f0pPsIdeRO5DJ6E5245Ad3DLzlrPDbKx8KfL7/kD2lV8i7n0GIxYmq+ejVVyG45oPYq+ZNSmHGUm9bNTMf7vZUxFqAH9Zv+u3YQYWsU6YfhmUnDP8a1N94CuFjp3QW2cGIq48dFEinUjQkCyiLZGlXAgSDLfhycnBm1+A0N+PYxgGRlZHKXEP/L1//34kSUKWh17ifMXF9GSzdESjuJuaANOhW3S78d98E7m3v3v878MocDrLmTHjP0hneshkepAlNw5H+dnxebawOJZUGF75LqRjkDv9aFbBmWM+duh5U4Z6/i0TO06/WMKI6BrYptLhxsLi7GJUwcX73ve+gd+3bdvG73//e97//vdTU1NDU1MTDz/8MDfffPMUDfHsQtV0MpqOU5HO6snGzTNvRjVUHq99nJZoy8B7UeAs4D1z38PykuXDvs7QDdNFPKHi8CjkFLtO//cxdzrKzd+Em74Bho59uNKhcWDEe+ne+H/01u8iFE/R7ZxBYOaFzJ03j8LCMYoKFMyC1V81VxvDLYBhBheK01RiufwLw9dQt24xpWZjnWZ5VCoCqT6MaCtp3GyKzKRJmIXPnURLayiajXBXJ9lMhtzSckQ9jNhXhyQJOGtMV25d14nH49hG6JsQBAHR7cZ+yy3kqypaby+i243r/POxVVSM8V0cP3ZbPnab1bxtcRZTt9b87gcqh14f7B5I9JrqTvNunlgPRtlS2PEns3TzOFPOgUWQOddbilEWFpPEqIKLu+++e+D3hx56iCeeeILly49O7m644QbuueeeyR/dWURtd4wndrSx7nAPqm5Q4nfytgXFXDWvCIcyOZPJ0wlREHnX7HdxReUVbOncQjwbJ9eRy3nF5+FWhlEVAtoO9bH75RZCHQk0TUeSRfLKPCxaVU5hle8En8EUIAim/8QkYOx/kugzD6JEOykyDIoFgZmR12jqXcdTB6/lkiveRk1Nzdh2WrkcbvudqcrS1wSSAmXnQfHC4W/aasZctYx3969a9ldp6iqpvl4ynbW4XQpuZwEaGh1iE6WpatxaAD3ch2I8iUvfgkgc0a0grvkjzL0Bcen7sNvtxGLDS7oa/aUPzpwc/OefP7ZzHAOGYdDR0cGBAwfo7u5GlmWqqqqYNWsWnhHUqiwszir6ms3J/ZuVK4VbQE0NDQrGQsVyKJoP7dvBV3Z0X7pq7t+Va0rSWlhYTApjbugOhUJo2uC6Z13X6e3tnbRBnW1sbQrx0FP76Iml8doVZEngcFeUH7wQZWtjiM9eM+esDDAA8px5XFV91Vs+r/VgiNf+VksmpeL22ZAUETWj0VkXZl13kktum0VB5QhqRmcbTRvIPP8NMtEgSTkPUVZMQzo9xQztIEZI4pVXXBQVFY19Emxzw+xrRvfcljcgWN9vmndM+5cok0hmMAyRcrGJFuNcdEEmbOslI6XIyQSoyf4Dp3oY0ZOH6K9GdIiQCMGmXyJE2pk961o2bNyEruuI4uDWslQqhc1mY9q0aWM7txGIpVXWHephT1sYw4BZxV4umZFH3f7dbNy4kXg8bjadCwKNjY3s2bOHq666iqKiokk5voXFaYvcL/JgGMMvQOiq+RxxgiVLsg2u+jo8/2Xo2A16lgGFOm8xXPofUDB7YsewsLAYYMzBxYc//GFuuukm3vOe91BZWUlbWxt/+MMfuPfee6difGc8qazGD148RCiRpTrPjdh/gc1x2UhkVNYd6ubJMj/vWFp+kkc6Nhp64mxv7iOj6VTkuFhalYNNnhr9AF3T2flSC9mUij/fMVAGZXPIKHaJcHeKXWtbWPXeOad/idREMQzY8Sey8T6igg+b3H/TFgSykhMBnWq9nj3hRmpra1m8eAplbsMtZvO2MlTMQM1k0LHhMFLYjSRJwQwMk1Icv20nsr6TiJxDQekx3wtvEaRdUPsC8y9fxaG8PHp7e3G73djtdgzDIJFIkMlkWLBgAfn5Ey9JOtAR5VtP76MpmBjoBX16dzuPvGIwPb4XvxoCGPjciaJIR0cHL7zwAu985ztHLN2ysDgrKF8GWx4xy5KOl7I2DEhHYOY7TdGIieIrhZt+As0boHWrGWDkTDMFJVxnsKS5hcVJYMzf2C996UssXryYf/zjH7z44osUFhbyk5/8hHe84x1TMb4zng11vbSEkpT4HAOBxRFcNhlZyvLMng5uOqcUWTr1xb3iaZUfrTnMusM9xDMqAiCJAtX5bj56xUzml/on/ZjdzTHCXQlcftuQ4EEQBJxehZ6WGOGuJIGi4b0zzhoSQejYQVy3I0pDs2EZ0YUn202x2jL12UjJZgYXhj44cwFIsoyBjoGMdlwpWJFWbzagD1cmYfdCrAtX2wauueY+1q1bR2tzM9GuLtA0nIrC4nnzuPDiiyccaPYlMjz0lBlYVOQ4B76fmm6wp6GdVjWX1Z4kfuVoI6mqqmQyGdrb22loaGDWrMlpyLewOC0pWWyWUx4x2LR5zAyGloVIK7jyYcEEm7mPRbbBtJXmj4WFxZQxruWAG2+8kRtvtOoTJ4PWviQYxoir+l67TFckRSSlkus+tVc5DcPgv58/yJr9XeS4bBTkms3UqaxGbVeMbz65j2+9fRGVeZM7wU8nsmiqjqwM/x7KNpF0QiWVOMlO1qcCWtqczIvS8KqLggAIiIZKIF4Hr+01VxW9xeYKX2AS5X8rzgeHz9SYd+UN2uTy+sjEWugxiskwOIiwaVE0Q8TlGyFQlWSIdZGTk8MlOGjYdohIKoKYTZATi+La+Aahujry7r0X0T6C98YoePlgN82hBBW5TuRBpVcGDi1BxLDRrAXwK6GBLbIsk81mSafTdHR0WMGFxdmNIMDlXzT/bdoAsS5AMCuW/OWwsr9cKZPoN9fzwZHvmq5DNmGqy01GZsPCwmLSGPM38vXXX+dzn/sczc3NA42RR6irq5u0gZ0tKJKIwchSoKpuIInClJUUTSZ72yO8VtdLvseG13G0RtahSFTmumjoTfDUrnbuu2yMjcJvgd2lIMkiWlZHtg1djdcyOqIk4HBZUoO48sGVjzvSRyItYWAgHOOOLepZDAymZQ5SUbsbGgzMO70BW38HS98HS+6cHFUVfznMugZ2/snMXDgC5n51FYcRIevwsT9YTFKN4nB7QIBULEZUEwm4FBzeEZr0NRWNAMGHXyO5u49c9yXk+QTQejHSh9H7mog8/QyCYiP/g/eNe/hbm/oQ4LjAAjLpNAIGkmDQlnWywBEatF2WZdLp9IgN5xYWZxXOAFzzHejcDS2bzSAiUAnVl0Cw1lSTa95oysX6SmHmlebvB58x1aRkO9SsNjMcOdUn+2wsLCwYR3Bx5513smLFCr7whS9Y9cKTwLmVAZyKRDSl4nMOnvwahkE4lWXV7EI89lN3ZSaczLKxrpendrXTG02TVzS0CVgUBNw2iZcPdvFvK6cjDuO0PF4KKjz4Cpz0dSTwHdNzAeZ7mIhkKJ4RwF84AbWRMwXZBnNvwNH7/7BlNDJZAUWWzffMMHCqIXRNpVDqRvHWmJkFQTCzHbFu2PhT8BTBrLdush8VK+43SyAOPwfBuv7yKAPRU4Rr5efx7+kjvGs7fV0dANhdbozpq3Gl1iBqaRCP+z9NR9ENmUhDDenWCIauIio6CDIoJQhSAEkUMDSN6Esv4b/lZpTi4nENXdP0IaWMgBmwCQLoBjrDbO9flPH7J79E0MLitEQQTFW54oVHH6tdA2u+afrgOAKmolT3AVO+VhDBV2JmMtJx2P57s7TqbQ+ZqlAWFhYnlTHPWIPBIN/4xjeoqqqaivGcddQUeLiwJo8X93UhCOCxmxM9VdfpCKfwOmRuXFx6soc5LIZh8NiONv64sYneWJpQIksklWVXa4TSgJNin33QRN8miaRVg6w+eV4NAKIksuiycl7/Ry2RnhQunw1ZEclmNBKRDA6PwsJLy6xm7iMsvBW5fTsFh14iloiTSstIaNiMNCnRhUMWceaWIDqPmfwKotkwHWyAXX82Vw8n4/1UHHD552HxbdD0uqlD7y0mlS0huuY1KrduIU9VSVWU4Vp6LqWrrsDn98LTKWhYD06/abhlGJAMYqRjpMULSEWnocdrEUQBBAWMrPkjesA2BykQJNvcTHLHznEHF7NLfLxW24tuGIOCDEVWECUZTRPJFRKDspKGYZDNZpFlmTlz5kz8/bOwOBNJ9sGr3zPLoXJrjl5rUn1mZkMQzZ4tZ475uFFgKs+98p/wjl+aLt8WFmc4PT09LFu2jJdeeonq6moAPvKRj/DDH/5w4Dk1NTUcPnz4hI9tzMHFJz7xCb761a/y85//fFj3W4uxIQgCH7liJppusLE+SE88M7DWWeC1c9+lNSyuCJzMIY7Ic3s7+enLtQgIlOU4sSsSiYyKqus0BuOIAhT5jioBxTMqNYVebFPQmF4+J5cLbxHY/XIrfV1xdNVAUkQKq3wsWlVxZvhcTBY2N1z9ILbKx/Ht/jv2niYyGvTlLcGTV4K/7m8I3hGUlFy50HPIbLb0T6KCWV6N+QNE17xEz0+/gx6JIrrdOAUB+849CAcOoekCxrvehXDV12Hjz0wH375mc/LhzEWfdhORvRciuEQwDITjfUH0FEgBBDkAQgtGJjPuIV82q4B/bG2lPZyi1H80Y6YoCpriwq4mqVKiZLNZBEEYyFgIgsC8efPIy8t7s91bWJy9DGeup6tmT4ZkA0ODaCd4ivv9f0TwlkD3QVMJqmLZSR2+hcVU09PTw/XXX09DQ8Ogxzdv3syTTz7JihUrAJCGEW45EYw5Oqirq+Opp56iurqa5cuX4/MdnbT96le/mtTBnS14HQpfun4e+zuibG4Mkc5qlAacXDQjH7/z1OwTyKg6/7e5GV03KM8xS1Ny3TZaQhKaroMB7eEUBV47oiCQzGhoOrxtQfGUZRDKZ+dQOjNAb2uMdELF6VHILXVbGYvhUJyw6F3IC9+JnE3ilmzkSDLs+gvUizBMOQ9grggahlnKNFHSMWh41QwOZDtUnE9W9dP78MMY6QxKVdWg/zu1t5fQn/6EY948nAsXmtr0S++B3kPmeAvnoHaKGLsOIfoUBFnGUFWEQRdXFQQHuioiyBJKacm4h1+R6+JDq2r44ZrDNPQmcPX3+yQyGh6PhxV5SYrTAprmQNM0DMNAkiRKSkpYtWrVuI9rYXHGE24x/z3WXE9Nm/KxogKGAGqyX2mu//ttc0GsA8JNVnBhccbz7ne/m/e85z1s3Lhx4DFVVdmzZw8rV6486UatYw4uqqur+dCHPjQVYzmrEQSBuSU+5pacHivsBzqitISS5HuOqu3YJLG/cTuOquvEMyqdkTSGYZDRdFbU5FJVHGJDexN+m59ZObOQJjl9LYoCBRWWWd6oEQTzpnyE3OnmymA2YWY4jicVNpWdvOOflANQ/yq8+l8QaTP/NnRQXKiZQvRwBLl82pCgUM7LI9PYSPSFF83gAsBTYP4cOR1bDEESQReQ8vPJtrYi6NoxZRISGDpaTye26mqcixeTTqdpbm4mnU7jdrspLy8fdVb2irlFlOe4eHZPB1sbQ2iGwWWzA1w9v5jpOTJ79+5l//79pNNp7HY7c+bMYd68ebjdw7vMTxaGqqKn0ohOx3HBlYXFaYBs75epPsZcT+hf9DCMfldvaXBp5pHnS1YvqMWZz8MPP8y0adP46Ec/OvDYrl270HWdc845h9bWVi699FJ+/vOfU1k5iSqPo2TMwcUDDzww8HtPT8+kGFFZnH4ksxqabqAcV+JU6LVjk0Xa+hIE41mSWY2qPBfzp/XQqj/G1zY2ktWyKJJCta+a2+fcznnF5014PFk9y9bOraxvW093ops8Rx4XlV3EeUXnoUinZvbnlKTkHCicB23bIKdq8MphJmH2RMy9YVjju1HTsRte/JppkOUvB0kZMMxSOraQU+EkJkwf9qWCw0H64MERd60Uu5HznWQ748jFReixGFokjCCIIEkgOTCSXUheg7z77mP3vn1s3bqVaDRq7l8QCAQCXHjhhUyfPvwYjmd2sZfZxcMHtMuWLWPp0qWoqoosy0PcwiebbGcXkaefJvbyWvREEsnrxXvF5Xjf9jbknJwpPbaFxaRRcT5sPc5cT3GA3QOJPjOx6ikc7I+TCILDD2UTv59YWJwsIpHIoL/tdjv2YSTTp02bNuSxvXv3Mnv2bH74wx+Sn5/Pxz/+cf7t3/6NZ555ZsrGOxJjDi4ymQyf/exn+fnPf04ymcTpdHLffffx4IMPWupRZxHFPgcORSKWVoeUbgWcCoLgIuBU+drNC9Btdfy/bb8nlolR4CrAITlIaSkOhQ7xvS3f45PnfZKlRUvHPZakmuT/bf1/bGjfgK7rKJJCVsuyvm09y4qW8bGlH8OlnOXmeaNFFOGyz8IznzXVmxSnuRKYiQMGTL8MFt8+sWPs/iskg4MbNQUBHH500Y3TGySVCqOqgaGv1XWEN7nOCKKA+4Jiwk83oEc0bNOmowV7UXuCGIaCgI59loOcW7/KwUiEdevWAaZykyRJqKpKX18fLzz/PJdWVFAiikg+H85zzkF0je8zJIriCbk2Zlpa6PzGN8g0NCK4XIh2O2pPD8FHfkt840aKv/hFZGsxyOJ0oHgRVK6A2hfNhQe717xGeIog3muWQrn7M5aGYTZ6p8Kw+HYSTj9vNK+lJdqCLMosyF/AvLx5iMKpL+duYVFRUTHo7wceeICvfOUro3rtHXfcwR133DHw909+8hOmTZtGJBIZ1MJwIhhzcPHJT36SzZs389xzz1FTU0NtbS2f/OQn+dSnPsUPfvCDqRijxSlIZZ6LJZUBXj3Ug9OuERf3E6MWnSx2o4hoYgYXT5vJuZV+Pvvqn4hlYlR4KwZKXZyykwpvBc3RZh7d/yhLCpeM++L/pwN/Yn3regqcBYOCiEQ2wWvtr1G4v5APLPzApJz3WUFeDdz4Q9j/hNkwnQpDyUyYcy3MvHpiWYtsEhrXmyuMw/TCCL4CxHgPNrETlcCgbYauo6fTuM5/83pqx4wcuMog+no7WiiFYAuglAWQfAqeC4pxLiwik8mwde1aYLAkrCzLeFSVYGsrG3ftYkVdPYIgIBcVkXP7u/GuXn1K9vAYhkHvw78g09CIUlExUAol+f0Y2SypffsJ/v4PFH7so2+xJwuLUwBBgMu/YC52NKyHePfR60X+TDOgSPSYPheGYZZwzn87O2dewo/XfpyOeAeGYWBgYJNsLMpfxEfO/Qg5Dit7Z3Fq09zcPCgQGC5rMVoKCwvRdZ329vZTP7j405/+xLp16wacZYuKivjNb37DJZdcYgUXZxnvv3gaB3ub2Jv9XwylA0EwMAwBzdBw5HqZP+NfOdxnoz5cT74zf8ikTBAE8p351IfrORQ6xOzc2W96PMMwaIu3kVbT5Dnz8Nv9hNNhXmp6CbfiHpKdcCkuvKqXV1pe4R0z3zGxG0siCPufNI2bkiHTzGn2tTDrbYN7Fs4UvMWw7APmz7F1zxNFy5gGWOLwpWpybi5qj4LeGUQXM4j9K/6GqpJtb0MpLMR7+eVveRjH7FzsNQEyzVH0hIrolLFVeBH6XdxbW1uJRqNDLrhqMEi2sRG7IBD2+UhPm4Y3lULt7qbnJ/+DoCh4L7tsYu/BFJCpbyC1ezdSXt6QHgtBUZD8fhIbNqD23GFlLyxODxx+uPoh6No7yFyvp2guO9o24Gp+g0A2TWXeHNwzrqJRUfje618hlApR7C5GERXT50hNsKlzE9/f+n2+tPxLyKKlcmlx6uLz+cYdCHz6059myZIlvOc97wFM02tRFIdkQ04EY/6W5ebmcuDAgYHgAuDAgQPk5uZO6sAsTn1KAzbyKp6npbOLbCYHQ5MQRYEct4LNHuXxxt+j2N5OVs9il83oO5FN0JfuI6tnUUQFn81HRssQyUTe9FibOjbxj8P/4HDfYTRdw6W4uKj0IubmziWcCVPkKhr2dX67n/Z4O3XhOpY6xll6FWmDp/4DuvebJnSSw+wbaN9pGjdd/eDRuuAzkQkEFkYmQ2r/fvREArmgANu0agRvsVly5QwMcygDKS8fIVGFeqATNA0DEEQRpaSYgo99DKVkdM3kgixinza8UV0mk0HX9cEyfYZBtq0NQ9eRHU6ygkBWEBAUBaW0lExLC33/9xc8F12EoJxafTzZtjb0RBxlhMBB8nrJdnWRbW+3gguL0wdBME3xiuZjGAZ/PfRX/rn+C4TT4YGn+IN93NpXTleii55kD1XeoypzgiDgVtwICOzq2cXunt2cU3jOSToZC4upZfHixXzxi1+kqKgITdO4//77ueuuu3CNs6R3Iow5uPjGN77B7bffzp133sn06dOpq6vjD3/4A4888shUjM/iFGZb1zZaE/XMLaxEEmyomo4siciigGF4aIo2sbt7NzbJRjKbJJQO0ZXoQjM0zBkjCAi4ZBdeZeTJ+cvNL/M/O/6HhJog156LrMjEs3Eer32cje0b0XRt6k7SMOCV70L3XghUm83HR8gmofE12PIb02naYgDDMIitWUPfX/5CtrXNlIR12HHMmUv+pcuw9Rwy+ziOVaQyDIi0IuZVkvuBX+LYvpfUvv2gm70T7otWIE2SvJ7H40GWZTKZzEA/hBaPYySTiDYbKUFANgycuj7wGjk/n2xzM6kDB3EuOLVcgAWbYqrnaBoMo3RlqCqCKL5pv4qFxanM0/VP84d9f8Am2Sj3liMKIpqh0Zvs5Td7fgOAWx5eetyluOhKdrGje4cVXFicsdx5553s2bOHd7zjHUiSxJ133smDDz54UsYy5uDi1ltvpaqqil/+8pe8/PLLVFZWsnbtWpYuHX9DrsXpyb7gPjRdwy6ZWQn5GFlZQRDwKB4aog1M90/njfY3SKgJJEHCITkGTMXi2ThJLcnO7p3MyRvqWBzPxvnd3t+R0TJUeI72bDhkBz67j6ZoExgQTofJdw5dkQ2nw/hsPqb7R6f8M4Tew9C6BdyFgwMLMJud7V448DSce5eZxrcAIPrsc/T87GcYqoqcl4dgs6EnEiS3baO9LZeyq5cg924zJ8R2n6lfnwqDMxcu+RSiNw/PJZfgueSSKRlfSUkJeXl5dHV1kZOTY36uVNU0uhNE0pJIZSqN65jgQrDZTInXeHxKxjQRHPPmI+floQaDKIWFQ7ZrwSBKRQX2USpgWVicSqS1NP+s/SeSIFHgPCo9LQkSha5CWqOtdCW7qPSOLLkpIJDVJ8Gfx8LiFOKIOesRHnroIR566KGTNJqjjKuDdtmyZfz0pz/lySef5H/+53+swOIs5fgP9fEcCSDePuPtpLU0WT070LSt6RopLYVDdlDgLODZxmeJZ4dO2jZ3bKY72U2hq3DIipQiKvgUHwICsWyMRDYxaHtSTRLLxlhZvnL8/RbBOnOF3T5CDaTDbyqV9DWNb/+nKb29vRw8eJDDhw8Ti8UGbdPjcUKPPgq6jq2sDNHhQBBFJI8HpaICtaOX3uZquOSTpq+GrpqSt3NvhOv/G6a9SUCh6xDrhniPmekYJ6IosmLFCpxOJ8FgkGQyiSqKpBWFiCTiVTXmJpODD51IIDocyAWnXlmR5HHju/56jHQaNRjE6A+KDF1H7TabYf033njKlXNZWIyG/cH9dCW6yHUMX36d5zTd7kPp0LDbj2S3S9wT9OexsLAYFWPOXEQiEb7yla9w0003cemll3LttddSXV3Nd77znZPuCGhxYqn2VyMK4kD/xPHEsjGWFS3DbXMTsAdIaklSamogyPDZfJR6SnHKTjrjnRwIHuDconMH7aM31QswYhOeU3ZiYDAndw67enbRk+wZkKIVRZHlJcu5Y+4dw752VBzJxhzrBHssug6Ig/0gTjWySWhYB90HIB0Fb5GpBV80/xhzudERjUZ59dVXaWlpIZ1OIwgCTqeTWbNmsXz5chRFIbFtO2p3N0px8ZDXC6KIFAiQ2LId9f3/hrzwnZCJmnK3inPkA+s6HHgS9vwdgvWAAPkzYP4tZlP9OPpCysvLufbaa9m6dSutra1kDAPR5aKitY15ioLvmODF0HW0nh6c556LbRh98VOBwNtvQU/EiTz5FNnmZvM9MQxEn4/c29+N9+qrTvYQLSzGRVpNo+naiPcBWZRxyWZdeSwTw2M7OhcxDIPORCd5zjxWlK44IeO1sDjbGfOM6N5776W9vZ0PfvCDgNmd/q1vfYv777+fX//615M+QItTl/OLz6fMU0ZLtGWgBvYIoVQIRVRYXbUaVVexSTYqfBVktexAMOJUnAgI6IaObuioujrkGC7FhWEY6IY+rFRtRs9gk2x8Yukn2B/az7qWdfQke8hzmiZ6y4qWTcxEr3gxOHNMXwZ3wdDtiV7IqTQ9G05FOnbDmq9D90HzHLJJwDAn8uXL4IL7YOaVo9pVMpnk6aefprOzE5fLRW5uLoZhkEwm2b59O8lkkiuvvBI9EgbDGHaVXAd6fF56NJ3OzZspmTOH8vLywY3Vx2MY8Nr/gx1/Msfu7M9Cte2Ajl0QaoQL7h1XgFFSUsJ1111HJBIhnU4jd3YS+e5/obY0owVyEOx2MxvQF0IpLCL3fXefklK0AIIsk/e+9+FdfSWJjRvRY1GkQADXhRcOWyplYXG6UOwuxqk4iWfjeG1D+/Pi2Th5jjwWFy7mjY436Ev34bF50A2daCaK1+blAws/YEnRWlicIMYcXDzzzDNs376dqqoqAFatWkV1dTXnnWe5Yp5tuBQXH17yYb63+Xs0RZuwS3YkQSKpJnFIDm6ecTPLS5bTk+zBpbiIZWLDXtyjmShum5sK71C5tKWFS/HavIRSoYHU9xEMwyCSiXB11dUEHAGWlyxnecnyyT1JT4EpObv9D+bqut03sCJsaqzrsOBWU0XqVCPaAc99EYINkImYUo6KAxBATUHTBvM5mTjMv/ktd3fgwAE6OzsJBAIDwYAgCLjdbiRJora2lgULFuDzmx4WRjY7KMCIiyKbPR56RQFNEFD270eqraWgoIArrrhiZMW5lk2w66/g8B0NLMAsSYv3wI4/QvVFULxw3G/VgPRfQQGuL3+Zvr/9lcSmTeiRMIJiw3v55QTe8Q7sNadoEHkMtvIybOVvP9nDsLCYNCq8FSzKX8Trba/jkl1Ix2RcNV0jlA6xsnwln1j6CdY2r+X5xudpjbYiCRKXV17O1dVXMy9v3sk7AQuLs4wxBxcFBQXs3r17ILgA2LdvH3l5eW/yKoszlXl58/jmxd9kbfNaNnRsIKNlmBGYwaqKVSwuWIwgCBS4Criw5EKebXwWj+IZlEnI6llC6RCXlV9GiWdoPWyBq4Brpl3DXw7+hZ5kDzn2HCTRDGC6E90UOAu4rua6qT3J5R80m41rXzDr/QUB6HeNXXo3zD9FJ3IHn4Fws1n6lE2C4jq6ui9KkE2ZZVKbHoaay83J+5vt7uBBJEkaNstgt9uJx+PU19dz4ZIlyEVFZmlUaSkAWQE2eL0EZQlnPIEjPw97QQHZbJaOjg6effZZbrrppuEl8w4+ZwZD/rKh21x5Zl/MoecnFFwMOpfp0yj61KdQe3vRwmEkvx/Zur5ZWJw0BEHgrvl30RprpSnahEfxYJfspLQU8Wycal817537XmRRZnXVaq6ovIKkmkQWZWzSKbjwY2FxhjPm4OKrX/0qt912G+9973uZPn06DQ0N/O53v+Phhx+elAH19PSwbNkyXnrpJaqrqwce3717N/fccw+HDx/mAx/4AN/5zndO2fKEs40idxG3zbmN2+bcNuJz7ph3B03RJg4ED6BICnbJTlpLk9EyzMqZxd3z7x7xte+e824UUeGp+qdojbWCYDZzz8yZyfsXvn/8SlCjRXHA6gfM1f2GdWag4SmE6atMN+uT/TnUdejcbTrW2v1QsshUtqp72TSri3WYPSHHjvPYErNYt+maPfuaNz1MMplEHkbmFMybvyAIJJNJRJeL3DvvoOdHPybT3Iycn0+r10tIFHDF4yg220A/hqIoBAIBenp6OHz4MIsWLRq682AtyCO4gguCea7Bujcd+3iQ8/KsoMLC4hShwlvBly/8Mk/VPcWrra+S0lK4ZTfXTruWa6ZdQ6HraOmfIAhDTFUtLCxOHGMOLm6//Xaqqqp45JFHeOmll6ioqOC5555j+fKJl6P09PRw/fXX09DQMOjxdDrNDTfcwNVXX82jjz7KRz7yEX7zm99wzz33TPiYFieGXEcuX7rwS6xpWsOapjWEUiFK3CVcXnk5V1Regd8+soyrLMrcNuc23jbtbezs3klaS1PoKmR+3vxB6fEpRRCg9Bzz51SiZTO8/mPoOQhq2pxo51TDee8HNQkI/Y7Yw7xPApiGI4ZZXvQWeL3eIcpQRzAMA8MwBkQdvJdfjqAo9P3fX8g0NtLqdIJhYAsEsJWXIx6ToZAkCVEUqaurGz64sHtMqdqR0FXzOSPQHEzw3J4OXjnUQyqrMS3fzdXzi7lkZj6yNC7BPAsLi5NAsbuYf1n4L9wx7w7i2ThuxT0ghT5pGIbZy9W80SwZ9ZWaC0meYXruLCwshmVcEjcrVqxgxYrJV11497vfzXve8x42btw46PGnn36acDjM9773PVwuFw8++CD//u//PmJwkU6nSafTA39HIm/u/mxxYvDZfNw842ZunnEzmq6NOTDw2/1cUj41vgenJa1b4dnPQyJkKkApLrN8KFhnNnHnTDP/FkQzu3H8PNowQHYCxqg8OmbPnk1bWxvZbBbluGbtZDKJzWaj5pieBM8ll+C+8ELShw9je+01bNEojoLhb9CSJJHJZIY/8LRLofF10LJDvUbUDCBA9fCfi92tYR58ah/t4RQuRUKWBDY3htjaFGJLYzEfWz3TCjBOABlVp6E3jqoZlOc68TksSdyzFk0FLT24THOM2CX75AcVAOkYrH0I6l8xS0n7K2DZ9Eu48N9h3o2Tf0wLizOQMd9Vs9ksDz74IMuXL6esrIw9e/Zw/vnnU1tbO+HBPPzww3zkIx8Z8viOHTtYvnz5QD32okWL2Lt374j7eeihh/D7/QM/FRVDG4UtTi4nLONwpmIYsPlXZlN57jTT6VoQTBWonGozqIi2g81pPqZnB/tCHMlyCKJpXFd10VsecubMmVRWVhKJRIjFYqiqSjabJRwOk06nWbBgAfn5gz0gBFnGMWcOhbNmoYviiN4o2Wx25L6tmVdCwRzoazRXEo+cfzoG4SZTUnf6ZUNelspqfO/5g3RGUlTnuSj2O8j32KnKdRFwKjy7t4Pn9na+5XlbjB/DMHhiZxsf+sNWPvbodj7x5+184JHN/OzlWiIpy9DsrCJYD69+D357k/nz5/eaCnCZxFu/9kTx6n/BwWfB5jE9eHJrzOtpJm5ua1h/skdoYXFaMObg4kMf+hB//vOfueeee4hGo7hcLlasWMG99947qtfffPPNBAKBIT8/+tGPmDaCfnwkEhm0TRAEJEkiFBreMOdzn/sc4XB44Ke5uXmsp2lhcWoTajBT9+7C4Vf/vMVmb0jVSvNGCaafRDYJ6bipcnVE+WrJneB+694Cm83G1VdfzbnnnouiKMRiMRKJBD6fj0suuYQLL7xwxD6omTNnDjR9H08ikUBRFGbPnj38gR1+eNuDpjdHMmRmZoJ15vlVLIerv2kGV8exsT5IczBBqd+JeNy4vA4FAXh6VzuGYZDJZAiFQsOOz2L8/Pb1Bn7w4iFaQgn8Tpl8j41URuNPm5r5xhN7SWSGyk9bnIG074DH7jdV9zJxQIDeWnj1u/Ds544uGpxMgnVQ9xK4ck1xi2PFL3ylkE3Azj9PyLzTwuJsYcxlUX/5y1/YvHkzNTU1fPazn0WSJD7zmc8wa9asUb3+Zz/7GcnjnG+BkWUoAVmWsdsHp0AdDgeJRIKcnKHSpna7fcjzLSzOKFJ9prSsa4TvjewwsxUzVsHsq2DLI2YzejZhyuY6/ObK3DnvgYXvHPVh7XY7F110EUuXLiUUCiGKInl5eSM2eh+hsLCQJUuWsHnzZoLBIE6naZiXSqUQBIHFixdTVjaMGtQRApVw8/9A2zbo2mve+IvmQ8k5I5ZWNPbG0Q2wycOvofgcCo29MV585TVa6g+RTqcRRZHy8nIWL15Mab/S1WhRe3rQIlGknADyMNels42Gnjh/3dqKS5HI8xy9Hhd4JbwOmS2NIV7Y18WNi8f2PlucZmhZePnbpux17vSjYhKu3H6Dz/XmpP28k9xD2brVzIbmDiMQIgimDHbnLrM/zeq/sLB4U8YcXFRUVPDKK68M1FYLgsCePXtGzDocT1FR0VgPSW5uLrt37x70WDQaxWY7tSTmuiIpnt3byUv7u4inVSpyXVw5r4hVswtHnOBYWIwLZy7IdrOkwDnM9yCbBMluSrWWn2dKzWZTpkt3otusdy45B2zjU1RxOByUlAyVDh4JQRA477zzyMnJYffu3XR3dwOmid38+fOZNWvWW6u/iSKULzV/RoEsioDZaD7cvjOqRrivj53bm3A7FOx2O5qmcfjwYVpbW1m9evUgxbqRSB86RN9f/0pi6zbT28Nmw738AgK33ortLC7JfPVwD7GUSnXe0M+YQ5GQRIHn93ZYwcWZTvMbZlbAVzZYpQ7Mkk3FAfueMBc65JO4KKhlzCBipOuQKJvPeTNxCQsLC2AcwcV3vvMdbr75Zn7+85+TSCT4xCc+wauvvspvf/vbqRgfAMuWLRskdVtfX086nX7TbMeJpq47xtee2EtTbwKHIqJIIjua+9jR3MeGul4+87Y5OBSrz8BikghUQtlSqFvbn8I/5qZtGOYqYf5MM4A4guKA0sUneqQDCILAjBkzqKmpIZVKYRgGTqdzyiSlF5b5sckS8YyGxz74UmcYBt19MUqIUpDrH+Td4XA4CIVCrFu3jrKysiHN68eS2rePzgcfItvdjRwIIPp86KkUkWeeJbV7D8Vf+iK2UQQoZyLBmCmqIQgCBhBPq4QSGTKqjk0WEYCOcGrE4M/iDKGv0VSsU0aQk7b7TBnteDf4y0/s2I4lUHWMJ5Bz6PZUBHzF4Mofus3CwmIQY15Of9vb3saePXu44YYbeP/738+SJUt47bXXuOqqq6ZifACsXLmSSCTCr3/9awAefPBBVq9ePayZ18lA0w2+/8IhmnoTVOa6KPE7yffYqcx1ke+x8crBbv6xrfVkD9PiTEIQTLlZT/HR/gMta6b1Q/WmNOvyD4I0LkG4KUUQBJxOJy6Xa0onlQvKfCypCNAVTZPMaAOP64ZBW18SQUuzwJcZch0RBAGfz0c4HKapqWnE/RuGQe+vfo3a04OtshIpEEB0OpFzcrBVVpJtbSH4hz9M2fmd6ngcCgag6ToNPXH2tkVoDibpiqRpDiZp7E0QjGfQdKuG/YxGsgGG2ec1HLoGgmRmWk8mFeebDdzRtqFjzSRAS8GcG8yyUgsLizdlXLU6NTU1fP7zn+fzn/88N954I5WVlZM9rkHIsswvfvELPvzhD5Ofn88///lPvv3tb0/pMcfCzpY+DnZGKfLZkcTBkyWXTcYuizyzp4O0qo2wBwuLcVA0D679DlRfbK62RdogHYHSJXDVN83Hz2IEQeBTV89mWXUOvfEM9T1x6nviNPYmcMgCy31hpo1gSi7LMoZhjOjrAZA+cID04UPI+fkIonkpNYAuRWabz8eGOXNY19bGwTfeQFXPvsbl5dNzcdkk6noSdERSiAK4bBIum4RDETGASDLLnzdbghtnNOXngc0Lyb6h2wwDkkEoWQjuk5wRkBS47LPgLTUXbKIdphpfqBFinaYk9hj60ywszmZGtazZ3t7O/fffj6Zp/P3vfwfgM5/5DP/1X/+FJEmUlpbyz3/+c3gDrHEwnFzljTfeSG1tLVu2bGH58uUjy1aeBBp7E2Q1HZdt+LfT51DoiabpiqSpyLVcQy0mkaL5cP33zWxFvNcskcqfdfJdw08Rct02HrxlIdua+9jcECKjapQGnCwrd/P84wfQtOHfJ103Vy4VRTFle7v2mp4agQpTOQZQu3vQkynkQrOPTAe2u900OuxogoCo62iaRserr1LV2cmVV16JwzFCacgZyLwSHytq8vjdhkYEA2RZBMNA1Q0ymo7XIZPrtvHEznZuOqcMt/3Uy7JZTAI51TBjNez5u1l2dESlTtdMuWzFBYtuG9s1K9JmloQmguY1r/oSU5L7GBLZBPFsHK/Ni0Me5feueCHc+APY8084/LzZY1E4B+Zcb/6MVNplYWExiFFdze+++248Hg8PPPAAAL///e/50Y9+xIsvvsjKlSt54IEHuP/++3n55ZendLDFxcVcd911U3qM8SBL5kVxpNphzTAQBQHFMuuymAoEoV+TfRiVEwtkSWRZdS7Lqgf3aFVUVHDo0KFh+z4SiQQOu51pie3wp69ApMWcDNncUHUxLP8gotuFoCgDTdyHnQ7qHA7suo7b0EFTMVQNyeWioaGB9evXc8UVV5y4Ez/JCILA5XMK+fu2VtKqTlLVwQBJFMhx26jOcyMKAp3RFAc6o5xbaSlsnbFc/DEzSK9ba6otCYKZtXAXwIUfgqpRmvIaBmz9rfmTCjPgcrf51zD/Zlj+7zTF23i87nE2tm8ko2VwyS4urbiU66ZfR75zFNmRnGq4+KNw0UeOGndaizUWFmNiVMHFhg0b2LdvH2VlZWiaxpe//GU+85nPcOmllwJw33338f3vf38qx3lKs7DMj9smE06qBFxDmz9DiSwLy/wU+Sx53NMaTTVvaLLd7GmwOK1ZvHgxLS0t9PX14fV6kWUZXddJJBJkMhmuKerC+cbzgGCWbIiKWXZ24EkI1uK46tsoxcWonZ3opaXU2x1IhoHdMAADPZNF9Hiw+3xoySR1dXWcd955+P1v7YZ+piAKAgGXQqHHQSKrYhjgVMzSKEEQUDUdw8DquzjTsbnhyq9C1z5oet0s4/QWw/RVo/LYGWDvP2HjT81rcO40U8jiSGnVtj/QqSX5WvIwHYkOfDYfDslBXI3zfwf+j62dW/nC8i9Q6Coc3bEEweqvsLAYJ6MKLmbNmsWLL77IXXfdxbe+9S1CoRAf//jHB7a/+uqrY5KlPNOoynNz0Yx8nt3TgSIJA+l93TDoiaVRJIGbzim1FFFOV9JR2P032P84xINmar/6Ylh4q1mWZDE2sino3g+6aiq0TJFmfHs4SUc4hUORmFnoQT4uc1hSUsLq1atZv349fX19A+WYDoeD5QuqmX7o72aTqfcY+WxXLti90L0f8dBTBG69lZ6f/IRQX4hEbg52QwdDR09nECQJW2kJ9DewB4NBOjs7z6rgoirPjceukMxq5LmHLq5EUioeu0xVf7loSyjBhrogsVSWXI+di2ryBnlkWJzGCILZJ1Y0b3yvVzOw81Hzd88x30lBAFcehpYhvvURIuXVVAamIfYr6HnwkGPPobavlj/t/xP3n3v/BE/EwsLirRhVcPGjH/2Im2++mXvvvRdRFPnd736H1+ulrq6OD3zgA7z++usDSk5nKx9aVUMio/JGfZDOaJojYYTPqXD3iiounWWZ7pyWpKPwzOegaYNpTGf3mKnyfY9B42uw+itQdeHJHuXpga7Drv+DXX+GSHu/S7gHpl8OF9w7thXMN6EllOCR1xp4oz5IMqshiSJVuS7etaycVbMLBwX51dXVlJWV0dzcTDQaRVEUKioq8NY/bWapjqvjBswyCcUFB57Ce8f/gaYS+fvfMVQVPZNFx0C021HKy5GOM9Mbrp/sTKbY7+DCmjye2d2Bxy4P8vvJqDp9ySzXLSwh123j56/U8uTOdqJpdeD6+chrNt67vMpanLGAnoPQ1zxi43dccaKEwizVJJqP89OQRRm/3c/Gjo28J/ke8pynTs+mhcWZyKiCi+XLl1NfX8++ffuoqKigoMCcKDudTm688UZ+8IMfsGDBgikd6KmO16Hw5Rvms7Olz5zUZDQKfQ5WzsqnPMdq4j5t2fUXM7DwlQ1u5nPlQagBXv0ulP5+eF10i8Fs/ClsfcQ0o3IXmBmgVBh2/wWCtXD990zn8AnQEU7x5X/uob4nRq7LRrHPQVbTqeuO8d3nDpLIaFy/aLBpm6IoTJ9+XL9KIthvqDVCn5TignQUIZvAd+21zF5+Idt+91uS6TQOtxvR7x9QkALTidxutw9cO88m/vWS6bSGkuxpCyOLAg5FIpXVyOoGi8r9/MvF03h0UxN/3twykMUQBQFNN+iOpfnZK3X4nDKXzxm7AavFGYSWAUMzrx/DkNZVBEPHLQ7vS+NW3PSkeuhO/v/27js+rupO+P/nlulFo95l2XLvxhhMMyY4dFNCIIEQAhtS2GwS2Odhn01+uwvJPmuySUg22bBZINk4DylkSaG3BWII1djGxt2WLVu9j6a3O/f+/hgQFpK7pJHt7/v10gs0d+bc7z2SR/c755zv6ZHkQogxdsTlOVwuF6eddtqQxyorK7njjjtGO6YTlqYqLKorZJEsTDw5GGnY8VQuqfholRBFySUcAy2w//VcNZTxlAzBnj/nNqhSbVB9GlSfPiH3tQCgb09u1MLuHfrJo6ckVz2mYxPseDq3S+9x+OOGVpp6Y0wq8gyWhbZpKu4inY5Qgoff3M+y6aX4nQffGA/IJTnW+7X5R0owjAQ4A2DzAOAqKmTu2Wfz9ttvY7jdOA5ILAzDIB6PM3369Am18ed4KfLY+b/XzOXFbV28uL2LvmiamkIXK2aXs2JWOaYFT2zqwKWrFHs+nOOuqQoVfictwTi/X9/G+dPLhpX6FqeQgtpcSdtkeMTRC5uRJKhq9NhH/qAnY2bQFA1HvvfTECe8f7taw+4Z3X3W0jENTqIJQBP0TkSICSARzNU5t/tGPq6/vzlUaJw3SNz3OrzynVwd9g+m2bz7cG437o9/C7xHuGBxnKRSKQbeepSCcA8Zby0uw0DXD3jr0e25jbaOM7mIpw3W7OrB59BHvAkt9TpoG0jy1p4+LppTcejGJi+Ddx7Klff96JoQ08htVjj/U0OSuUWLFhEMBtm9ezexWAxd1wf3t6iurubcc0+NfUcsyyKYCmJZFoXOQlRFxe+08YnTavjEacN3YH5tdy/90TTVhSOX+Sxy29nfl9ujZGqZFFI4ZXlLoeGC3Giy0//+5nzvMw286TjrfaVsVzKM9A7Yn+xneuF0JvknjVvIQpyqJLkQ4mB0R24I3syMfNwyczf34zklqmcXvPRtSA5AoO7DKQLpOLSuhf+5G6789wkzgrFr1y7efPNNZnStZ0YywUCqD21gAJ/fT6Cg4MN59DZXrkSlmc1NlzoG4YRBIp3FbR/59bqmggID8YP8PA9UUA3zPw3rfg7hzPvVovTcp6bxXiiZCnOvHdq+rnPhhRcydepUdu3aRSgUwu12M3XqVBoaGrDbT+7KM5Zl8Ze2v/Bs07PsC+8DC6q91VxUfxEX1l2IdpCfa8rIYloW2kHWVOhabopU2jjIDs/i1LHkC9C7Gzo359bA2Vy5EreZGGrJNFIzV5BueYbeRC9FziJURcUwDXoTvTg0B1dPvXpwobcQYuxMjDsQccpLZVMkMgk8Ng827TBTVsZIS6SFPzf/mY09GzEtkznFc1heMYNpTW+Bq2h4rfPEQK5yUM2S8Qty2+O5m9uihqHx2N25zd06NkLrOxNikfm+fftYs2YNhmGguANoGQ2brpM1TQYGBlAUCBQEck82klBQc8yJBYDXmVswnMpk8Y6wIVvWtLAsC7/rCN/2ltyWW3D+3gcL0N/f52LKBbka+L7hox+apjFlypThazhOQslMltd297JmVze9kRQDRgs96p9x+1oodPlQUNgT2sNPN/2UplATX5j/hRFv7KoCLpx2jVh65J9bNGngsetUFMgGZqc8bylcfl/ufXDn07n3YFchLLwB5lzDxz2lhNwFPLn3SVojrSiKgoVFibOEG2bdwDnV5+T7CoQ4JUhyIfKqJdLCM3uf4Y32N8iYGbw2LxfUXcBlky+jwDF+JTvXdqzlJxt/QjAZxKE5cjdGA3t4GZVb3S4uGtifW2Oh2XKjFcmB3LSpudeOXFFoLFgWNL2SW7cw0qe8NneuklXburwnF5ZlsWHDBtLpNIWFhfRkZjE18hYOK0Fa92AYBuFwBJ/Xh4aZ+/RxxmXHdU6vQ+ecqcU8ubGdQrcd9SNTo3qjKQo9ds6cfISLOVUVFt5IdsZFZNvfQbNUtKJpslkhEE5mWPX0dtbvD5KbmJehNRJHUc6krGgm1dN2outZfHYf4VSYF/a/wJKKJZxWftqwtmZW+JhV4ePd5gFcNm3IlLZM1iSUMLh6URVFnpN75EfkGKbBez3vsb1/O5ZlMblgMqdXnP7hWgl3EZx+K5z2udzaJ905+KGEBnxq5qe4cNKFrOtaRywdo9BZyJKKJfgONr1VCDHqJLkQebM7uJt/XfuvdMY78dq82DU7wVSQX2//Neu71vPNM79JkXPsF8D2xHv4j43/QSQVoc5XNzhVx7IsuhPd/CIQYHI0y7RQa+4FH5RQnfOJ3M6z48WycsnDQaqlDMqmxyeeQxgYGKCnpwePx4OiKIRt5bR6F1AfeQfVMkiqHtKZDOlQNy4rBmWzYdbK4z7vJ0+rZcP+IPv6Y5R6nXgdGplsbr8ZE7h5cS2FR3iTmkr30tnxJ3r7/kw2G0dVbBQqZ1HpvAa3e5wSygnqv15r4u2mfqoKnDhtGi2RFmyOAeyKh97+Yva11DF1chMAfoefgdQAa1rXjJhcKIrCX18wlXue2Mr+/jheh45DV0mksyQyWeZU+7lpqcyTPxV0xjr54fofsju4G8M0QAEFhTp/HV9d9FWmF07/8MmqmhtJHEGJq4RL6i8Zp6iFEB8lyYXIC9MyeWjzQ3TFu6jz1Q1Ol/Db/WSyGXb07eD3O3/PFxd8ccxjea3tNfqSfdT6aofU0lcUhTJXGc1GM6/MX8k09xQIt+Xm+dYtHf9PsFUVSqZBy9qRa72bucXDFNaPa1gjSafTmKaJ+kHVJEVhW9HFpFQPddENeLNBsqYB2VKY8XE4+2u5TySPU12xm3uunMMDr+5lW3uY3mgKXVMo9zv55OIarlxQdfhGgFSqix077yYW242u+dA0F6aZoqv7aQZC65k+/R/xeWced7wnop5Iild39VDg0nHacp8YJ4wkiqKiaRa6btDVW0ZdTSt2W259i0N30BJuOWibDaVevnPtfJ7c2M6aXd0kMyZFXjsXzalg5fxKAm4ZtTjZJYwE31/3fXb076DCXYFTz02DS2fT7Avt4/vvfJ9/OfdfKHWfeuWchTjRSHIh8mJ7/3b2DOyh1FU6bB62TbPhs/t4rf01PjXzU2M+PWpncCeqoo44H1xRFJyaky0Du2DRX49pHEdk1pXQui5XivbAPSGs96tW+SpyawLyzOfzYbfbSafT2Gy5NTSWotFYeD77/GfgizWRSSZYctH1uGadOarnnlrm47vXzqexO0pnOInLpjG3umDwRvhItLb+mlhsNy5nHergSJEXm62QRLKZ/fsfYM7s+1BOwcWhe3uihBOZIfv3aKo2uEGgTc+QTDmJxTzYAwNAbqqL23bo/X6qAy6+vLyBvzp3MolMFo9dG7arujh5re1Yy+7gbqo8VdgPqARl1+zU+mppibSwpnUN102/Lo9RCiGOhLxzi7zojHWSzqYPesPhtXmJZWJ0x7vHPBaF3KK/g7GwJk6FkakXwpyrc8lFf1OuVG60G/r35haXL/vfozICcLzcbjdTpkwhkUiQzWaHHMuoThoz5aSql1I+Y2wWwyuKwrRyH+dNK+X0+qKjSizS6V76g29g0wMHJBYftKtit5cSi+0mGt0+2mGfEBRFyS2UPWC38YCjAIXciOSHT8wdN0wDwzQ4q+rI1gHZdZUCl00Si1PMxu5cIY0DE4sPqIqKXbPzdsfbeYhMCHG05N1b5MUHi6azZnbE44ZloCkaTm3sK8TMLZkLFiPGYlkWyWySRWWLxjyOI6JqsOwuWHFPrkqVouYWNM69Flb+CKYsz3eEg04//XTKysoIBoNEIhFSqRTxeJy+vj68Xi/nnHPOh9OmJpBUqodsNo6ujzyfW1PdmGaKZKpznCObGKaXeylw2wgeUNI34CjEa/eSyiZJpjXstjRed4yEkaA12srkgsmcV31eHqMWE10im0BTDv4hgKZoJI3kOEYkhDhWMi1K5MW8knkEnAGCqSAlruHrB/qT/cwsmkm1r3rMYzm3+lye2PMEbdE2qr3Vg/X4TcukI9pBkbOI5bXLxzwOy7JoCjXxevvrdMW68Nl9LKlYwvzS+egHfoKuajD94txXNgOKlluPMcH4fD6uuOIKNm7cyO7du0mn02iaxuzZs1mwYAGlpRNz7rSmOVEUHdPMoKrDd/O1rCygoKmnZmnUgNvOx2eV87t3WnDbNTwOHU1RaQg0sKu3mQHDxFe0hY7EXmyajTnFc/jqoq+Oa/U3ceKZ5J/Ea22vYVnWkLVvH0gYCaYEpFKbECcCSS5EXhQ4Crik/hJ+t/N39Cf7KXQUoii5kYyeRA8OzcFVU68al+lIBY4C7lx8Jz9c/0Paom2DFUpMy6TYVcxfL/hran21YxqDZVn8dsdveWLPE0QzUTRFI2tleX7f8ywuX8zXT/s6XvsIuxPnaU+QI+X1ejn33HM544wzSCQS2O12XK5x3HTwGLhck/C4pxCJbkfXh/d5JtOH3V6C378gD9FNDDefVU93JMVru3vpjqawqQoZ0yJgr2bZVJUzZ3vRtDNoCDQwp3jOQTfQE+ID51afy5N7nqQn0UOZe+ge2wOpARyag+U1y/MTnBDiqEhyIfLm+hnXk86meX7/8zRHmgc/rSpyFnHjzBs5u+rscYtlZtFMvrvsu7ze9jqbezdjWiYzi2ZybvW541Kd5KXml3h016O4dBeTfJMG+yKeifNmx5t4t3j5+mlfH/M4xordbj9hdqhWFJWqqutobPxXksl27PYyVFXHsrKkM/2YVprqiquHJR5Zw6S9cYDufWGyhklBiYva2cW4/SfGdR8Nl13jG5fOZMOcAV7d1UNfLEWpz8H500tZWFs4ZK8KIY5Era+Wz87+LL/Y8guaw8347D4URSGSjqApGldOvXLiTE8VQhySYh24Ku8kFQ6HKSgoIBQK4ff78x2O+IiOaEduw6NMjCJnEWdWnnlKTaEwTIO/e/XvaAo1Ue0dPg0slAphmAbfX/79EY+LsdHT8wItLb8kle6B3HJlbHqAioqrqa6+YUilqGgwxRt/bKS3NYqVNQc3OXT57Cy+dBKT5hzhxn1CnOI2dm/kuX3PsbV3Kxa5TfQumnQR51afO+J0KXFymcj3ax/E9oXHv4B9lDf1TMfSPHTVQxPyuo+FjFyIvKv0VrLSe/wbqJ2oOmIdtEZaCTgCIx732/20RFrY0bdDkotxVFp6EYWFZxEcWEsm3YemeykMnIHdPnSNkJk1efNPjXTvD+MrcqC/X5nKNC2iwRTvPNmEp8BBSc0I09qEEEMsLFvIwrKFpLIpTMvEqTklqRDiBCPJhRB5ZlomFhYKh/4DmrVGrqwljp1hGGSzWWw224iVq3TdR2nJhYdso3NviN7WKL7CDxMLAFVV8BU5CPUk2fNutyQXQhwFhza8mIIQ4sQgyYUQeVbhqaDIWUR/on/EfT9iRgyH7mCSf1Ieops4BrrjtG7vJx5J43DZqJoeoKTGe0yfavb19bFlyxb27t1LNpvF4/Ewa9YsZs+efdRrQ7r3RzCzFrp9+KJlRVGwOzXadgaxLrdQZC2CEEKIk5wkF0LkmUNzsGLSCh7e+jDxTHxIgmGYBr2JXk4rO43phdPzGGX+WJbF5jWt7Hyrk3TSAAssYMdbHUyaU8zpl9cPGTH4KDNrkjUsdLuKoii0t7fz/PPPE41GcTgcaJpGMBjktddeo7l5N2ecUQjE0W1+AgWnj1gxamj7FhxiE0ZFyW2gblmHH50CMIwI4cgWLDOD01mD2z1ZpoUIIYQ4YUhyIcQEcGXDlewd2MubHW/Sl+zDpbtIZ9NkzAwNBQ3cvuD2U/YGs3F9N1v/0o5uVykodQ3uDp1OZNnzbg92l85pFw8f1Qn1JGhc30Xztn6yGRN3gZ36+UW82/QqsViMoqKiwT51uVxo+mYsfs/2HSZOZ24PC7u9lJrqGykru+yg/V9Q5gJFwcyaqCPsKp1OZqmc6h3x2IFM06C941G6up4kne4Fy0TTPfh985g06Uu4XGNbDlkIIYQYDZJcCDEBODQHf7v4b3mj/Q3WtKyhPdaO3+7n3OpzWV67/JSqnnWgbNZk19ouFMDt+3C6kqIoONw6WcOkaVMPs86uxHXA8Z6WCK//vpFYMInNqaHpKuHuBG+8sIl+Wydl1cVDkgW7fRde31/IZg3iMT+BQB1YWdLpHvbt/ymqaqO09KIRY6ydWcSWV9qI9KfwlwxdfJqMZVBUhSmLykZ87YFaWn5BR8fvUVUHTkc1iqKRzUboD75FMtXJrJmrcDgO344QQgiRT5JcCDFB2DQb59eez/m15+c7lAkj1JUg2p/E6T1gs0C9D9XRAoBLrSHU6aGnOULd++Ves1mTdU/vIxZK5UY63l/n4PTYiEczZOJZUpEs9uIP2szicq1HwcAyA5imSTZrYNNtOJ2VJJKttLX/N8XFy1HV4esx7C6dM1ZO4c0/NTLQk8Bu11BUhUwqi6opTD+jnLpZRYe8zkSima7uZ9A0L3b7h8/VdT+a5iER30dX9zPU1d5y7J0phBBCjANJLoQQE5ZpWrm1CqoCagyt6Ak070ZQYwBoWQ9e52wyma8CueSia2+Yge443gL7sAXUum5DUSAaSuItdKCoCrrejab3kzW9WFi5HdoPHNWwlZJMthGJbqfgILtyVzYU8LGbZ7H33R5adwYxDZOyej9TFpZSM7PwsFPagsG3yWajuJzDp3cpioamuentfYnams+iKLLbtRBCiIlLkgshTgGGaZAwEjg1JzbNdvgXTBC+YicOt410MoGz9v+hurdiZb2QrgDAUiK4ytYSMe8nm/0XNM1BpC+BZY5cvclrK0RTdQwjTdYw0e0aipJGIYtlaWSzWdxuN5p2YElZG5aVJZuNAxDuS9C2M0gqbuBw26iZWYivyEmgzM1pF08acf3H4RhGGOCgSYiqOshmE5hmCk0bXlFMiLESy8TY0LWBcDpMgaOA08pOG7GqnRBCfECSCyFOYqFUiOf3Pc/LzS8TTodxaA7OqT6HSydfekJsyOdw6UyaW8ye7c+huLZjZYrBer/+vWVhpLw4vW4S6c0EB96kpHg5qq5+WJ3pIzfrDs2DTytjwGonlU6h2VyYpg/TsmGacVTVhd/nH1LVKZuNo6pObHoJm15qYdc7naQT2dym3RZsebWNGWeWM+/8mmMuNWuzFb5/SeaQnb8/jCGB3VGGqjqPqX0hjpZlWTy//3ke3fkoXbEuYkYMwzTw6B4um3IZt827DV3VCSaDOHXnKbsuTAgxnCQXQkwU8X7Y/zokBsAVgEnngPvQc/UPJZgMcu/ae9netx2n5sSlu0gYCR7b/RhrO9by92f8PVMCU0Yt/LEyZ1k1weQuMlkTM6WjqCaWZWFZYHNqBMr8pI0ofX1/oaR4OeX1fuxOjVTcwOkZOkpjWRa+dD2OIh3DGqC/vx9FUVC1Uny+FrzeKlwu1wHPN0mneygILKZ1s4etr7Vgc6gUlDoHq1YlIhm2vtqOw21jxpkVx3SNhUVn09r2a9LpPhyO0iHHTDODaSYpK/34iImHEGPhzy1/5mfv/YxoJko4FcawDCzLIpaJsXrLap5tepYydxmpbApVUZlXMo+VDSuZWzI336ELIfJMkgsh8s2y4L3fwfrVEO/78HF3CZx+K8y7DiIdsPNZ2PcXyCShbBbMvByqF+c2UhjB73f9nm2926j2Vg+ZClXkLKIl0sJDmx9i1bmrJnyJW4dLp2wK9Pe6yKBjGFk0TcPts+MusKNpKkbWRiaT6zt/iYu6ucU0ruvKbWLn0lAUBdO0iA2kcDjsfOzSj2MvzNLc3Ewmk8Hlmk7GeJh0up102kDTnGTNJJnMAE5nJVUVf8UrL3ShacqwqlVuv51IMMmutZ00nFZ6yD03DsbpqKCq6npaWlaTSLZhtxWhKBqGESVjDODzzaGs7NJR61MhDiWTzfDH3X8kYSQIp8KYlolLzyXdlmURToVpi7YxkBpgeuF0slaWN9rfYHPvZv5m0d9wdtXZeb4CIUQ+SXIhRL5texxe/zGoOgTqcv81DYh2w+s/gkgnNL4I4TbQnaBo0NeYe2zRZ+GMLwxLMEKpEH9p+ws+u2/YGgtVUSlxlbA7uJudwZ3MLJo5nld7TFyuCuzO9wgU+bAYvhmdaaVxOCoHvz/t4kmYhknztn4S0czgFCanV2f66RW4fA58fieliz8cJUgkZtLe/nv6g2+QMcKoqp2yskuoqvwkoY4i4qEdeApG3r3b5bUTDaboa4tRXu8/pmusqrweXffT2fknkol2LLJomofy8pXU1tyEzRY4pnaFOFo7gztpj7aTtbIYljGYWACksqlc4QNya7kyZoYiZxEF9gLaY+3815b/YkHpAjw2T77CF0LkmSQXQuSTkYKNv879v//Dm2NUHfxVENyXSzBchVA0BQ6cFhPrhQ2/hLKZMHnZkGa7493EMjGKnCNPq3LrbvoSfbRH20+I5KK4+Hx6+/6MYUSH7ZhtGFEURaek+MMSvja7xtKrG5h+ZgUdjSEyKYNoMMVAV5xtb7Sz7fV2HB4bUxaWMOvsKmwODZerjoaGv6Uu81dkMiFsNv/gWoigEcQyrYOuqVDV3BQp0zCP+RoVRaG87FJKS1YQi+/BMtM4nVXY7SXH3KYQxyKeiWOYBpF0BO2A6mSWZZExM4OjnZZlEU1HKXLmNqQsd5fTEetgbcdaLqi7IF/hCyHyTCbwCpFPXVsg1Aqe0pGPqzqkwrk1GB+db+8pgWwatj0x7GV2zY6maBimMWKzpmWCkntePmWzKfr6X6Ot7be0tz9KNLoTy7KGPa+g4DSKi88nneklleoim02RNVOkUl2kM70UFy2joOB0DCNCd/dzNDf/nNa2X2Hz7mX2uZWoqkrrjiDRYAqHS8fh0UnHM2xe08Ybf2zEyGQHz2WzBXC7Jw0mFpCrWmVzaKQTI/dnOmlgc+j4io9/wbWq2vB5Z+L3z5fEQuRFibsEp+7ENM0ho4QWVu69wyL3uMLgKAaArupYWHTHu/MRthCnlN7eXiZPnsy+ffsGH9uyZQtLliyhsLCQu+66a8S/p+NBRi6EyKdMMjcF6mDlYY1kbk0GB1kX4fBB52bIGqB9+M+51lfLJP8kdgd3jzg9IZgKEnAEmFcybxQu4tiEw++xt+nHJBOtWJiAhaq6KAycwZQpX0fXfYPPVVWdhil34HRW09PzPOl0LwB2exGVpddRVXk9A6F32LfvflKpbsACy6Jdc+K0zWPXuouw2b1DdvG22TUy6Sxtu4I0b+1nysKDJHjk1nFUNBSwf0sfdqeGqn2Y6JlZk0Q0w+QFJXgLpZqTOPFN9k9mZtFMWiOtZMzM4NTKDxINExO7YkdVVNz6h2Vpc4UWLJy6/DsQYiz19vZyxRVXDEksUqkUK1eu5OKLL+aRRx7ha1/7GqtXr+bWW28d9/hk5EKIfPJVgM0N6ejIx410bsTiYH+sLSu33uIjoxqqonLV1KuwqTa64l1krez7T7cIJoPEjTiX1F+St/KR8fh+djd+h0SiGYejHLdrEi7nJHTNTW/fGhr3fB/LGjrFSFUd1NbcxIL5DzBn9veZM/t7zJ/3ALU1nyWeaGLPnvtIp3txOqtxu+pxuerRNR/9wTdwVD2C0zv8sxSbXQMLmjb1HjbmRR+vo6jSQ7g3STSYIhnLEA2mCPelKK7ysnBF3aj1jxD5pCgKn5vzOWp8NWStLIlMgqyZzY2EWh8mGQ7NQaHzwxG+UDqEx+bhtLLT8hW6EKeET3/609x4441DHnv22WcJhUL84Ac/oKGhgVWrVvHzn/88L/FJciFEPhVNyVV8ivaAmR16zMzmRjVsLlBH+KdqWZCKQO2ZIx4/p+oc/mreX+HSXbRF2miONNMcaca0TK5quIrrZlw3RhcFiUiYPevfZscbr9K6fQtZY+h0ou7uZ0mnunE5a1HV3GiCoijoug+HvZRQaD2RyJYR29Y0Nz7fbHy+Oeh6blSmu+tpDGMAp6MGVdEPaM+LlSnEXrAb1dU0Yns2h0Y0mDzsNXkLnSz/zEzmf6wWl8+OZYHLb2fBx2pYftMMPAWOI+4fISa6hkAD/7rsXzm9/HQsLBJGAsM08Nq86KqOoihUe6tzU6Esi4HkAOFUmGXVy6j11+Y7fCFOSOFweMhXKpUa8XkPPfQQX/va14Y8tmnTJpYuXYrbnRtNnD9/Ptu2bRvzmEci06KEyCdFgbO+klu4HWwChz+XTGQSkAzlSs4qSu54oA4+WCNhmbnytA4vzL7qIE0rXDr5UpZWLuXtjrfpS/bhtXk5veL0MdtAz8xmee/F59j++iskI+/vOq1pBMorOeOqT1I1fSaWZdHf/xc0zTPivg2a5iGd6SEU2oDfP//w5zTTBAfeRtd8I5bVVTU3KL2ozt1kkw3DjmcNE4f7yN4K3X4785bXMGdZNdmMiWZTUQ9Y5G0YEfr7X2cgtAHTTOFxN1BSshyX6/CjGrFYI729a4jH96JqLgoDSygqOnfYAnYhxsvkgsk88PEH+EvbX3i5+WVaIi24bW6qPdXsGthFf6KflkgLpmXitXu5dPKl/NW8v8p32EKcsGprhybmd999N/fcc8+w502ePHnYY+FweMjjiqKgaRrBYJDCwsJhzx9LklwIkW/FDbDy32DT72DPS5CJ56ZBLbwR5n8qN3rxwj9C7y5ycxLU3GOuQjj7q1B96CkIhc5CLpl8ybhcyqb/eZZN//M0ut2Bv6wMVdUw0mmCHW288qv/YsXnb6e4tgbTTKOoI7/95BIEhaw58ic2H2VZxvs7W4/cnstjJ5VUMLLD2zOzJlnDpH7eyAunw7097Nu0gUhvD7rDQfX0WVTNmIWqaaiOoftZxOP72N14L/F4boREQSXY/zqdXY9TV3cb5QfZp8KyLDo7/0hL668wjAiqasOysvT3/4XOzseYNu3/w+WST4JFfiiKwrKaZSyrGVqRLpVNsaFrA52xThyag4VlC6nyVuUpSiFODi0tLfj9H5YzdziOfERc1/Vhz3c6ncTjcUkuhDglBerg/Ltg6e256lAOf25U4gPX/Cc0vQIta3OLvEumwdSPQ+Gk/MX8EfHQADteX4Nud+AJfPhGptvtFJSVM9DRztZXX2b5Zz+P01VLNLoNbMNL5Vrvrw9xHrBvxaGoqguns4pYbPeIe0HYnKDbNSIDRZDI4HTroEA6mSUeTlNc5aV+/vDkYscbr7LhmcdJRqPv75Nhsf21NVROncF5N34Ot//D9SrZbIrGPd8lFtuDy1mDqtrevxaLVLqL/fsfwOWsHnEkZmDgHZpbVqOg4XbVD46+mGaGaKyRxj3fY87s+wbbFGIicGgOzqo6K99hCHFS8fv9Q5KLo1FUVMSWLUOnE0ciEez28a8KKWsuhJhIHN7c/hYO7/DHZ14OH/8WXPqvsOS2CZVYALTu2EoyGsXlH75IXFEUnD4f7Tu3kYhGKCu9CCwLIxsb8jzLskilurDbiikqOveIzqsoCmVll4BlYhhDF8ZblkU63UmguIbaKcvBglBvklBPkkwqS/X0Qs69fhpOz9Ab99ZtW3jniT+QzWQIVFRQWFFFYWU1br+fth1beOO/fz2kxN/AwNr3E4vqIUmAoig47OUYRpTu7udGjL+r+2lMM4XDUTpkWpeq2nA6KonFdhMKbTiivviodLqfeLyJdLr/mF4vhBDixLBkyRLefPPNwe+bmppIpVIUFY2839VYkpELIU5ilmVhpFKouoamj+0n35lkEhRQR1p8joXNlyRrRImFmymp+BgDofX09b1KRhlA131YVhbDCKFpHuomfQG7/cjfEEtLVhAOb6av789kjCC65sW0shhGBLutkClTvkbh4rnMOSdFT0sEy7QoKHNRVOkZtk7Dsiy2v/4KRipFoGLo6InN4cRdUEjH7h30Nu+jdFJufms0uh2s7ODi9AMpioJN9xEKbcCyrCHny2ZTRCLbhpTdPZCmObHSBtHodgoLzzzi/ojF9tLe8SgDA2sxzTSqaqewcClVlZ/E7R4+V1cIIcSJbdmyZYTDYX7xi19w6623smrVKlasWIGmaYd/8SiT5EKIk1DWyLBn3dvsevsNwj3dqJpKzex5zDjrPErr6sfknJ5AIYqikDUyQxIZzduFrXQ32HtRVdi19y6UHTXYs0vx+C7BdGwnY/QCKsVFyygvX0lBwcKjOreq2mmYcid+/3x6up8jkWxDUx0Ul59PedlleL3TAfAWOvAWHnoOazoRp2ffXhzekRdS210u4uEQnXt3DyYX1pCtxEaiDO7lMXTPEmuEx45PNLqLXbu/TTLZgc1WiE0vIGsm6e5+jnD4PWZMvwePZ/jCdiGEECcuXdf52c9+xg033MBdd92FqqqsWbMmP7Hk5axCiDGTNTK8/rtfsWf9WhRVwe5yk81k2PXmazRv3sS5n76ZurmHr8J0tKpnzsZbVEq0vxd/aVmuUoWvA2fNBiw1TSauguZkoKsb1daMEd9I/yuz8LhrWXzVrdTMmHvQT/CPhKraKS+7lLLSS3ILxhUN9SCLxg8laxhYloU6QiUryI1EKICV/XAfDo+7AUVRMc3MiGsjjGyUooJzh1XHUlUHXs90BkLrsNuGL7gzzRSKouHxTDui2C3Lornlv0glO99fv5E7n6a5sOkFxBP7aWn5BTNm/POIlbWEGEk6m2Zd1zp2B3djWRZTAlNYUrEEl+7Kd2hCnNI+ugP3lVdeyZ49e1i/fj1Lly6luLg4L3FJciHESWbPurfZs2Etbn8BdteHf/xd/gLCPd28/adH8JeWoaoqTp8Pu3N0bhBsDienr7yG13/3MANdHbj8HgJTtmCSIhN2AAqGYaDpDsCF3RehaHo/PZvcvPm7P/Lx2yopqTv25OIDufJ7x77nhMvrw19aSl9bC07P8NELI51GUVUCFR9WxikqOhtXWw2JZAsuZ93gTb1lWWQy/aiqnbLS4RW7FEWhrPwyQuFNpNP9Q6aCWVaWZLIDj6eBQGDJEcUej+8lGtmG3V4yLJFRFBWHvZhwZDOJxH7c7vojalOc2vaH9/Nv6/+NplDT4GacqqJS66vl66d9nWmFR5b4CiHGR0VFBZdffnleY5AF3UKcRCzLYtfbb6CgDEks4P3FxW4PXfv28sdV/8Tj9/0Lf/rOt1j7+O+JhwaO6XypeJxQdxeJ9/e0qJ+/iAs+90VqZs1F8/SiOCJgabhLMjiKQjiKIuieBIpqYmUd2ALdBCr9JMJhdrzx6vFe/qhQVJVpZ56DZVqk4vEhxyzTJNLfR2FlFdUzZw0+rmluGhr+Nw5HJYnkfpLJNpKpThLJfViWQXX1Z3C7pxCP78cwIkPaLCo8h+qqT2FaGeKJJpKpDhKJ1lyi4q6joeGuEddyjCSV7iZrJtE0z4jHNc2DmU2STh9+R3IhIukI33/n+zQONFLqLmWSfxKT/JOocFewP7yf76/7Pn2JvnyHKYSYYGTkQoiTiJFOEe7txu5yDzuWjMXob2shk0xiOF14nEVkUkm2/PkFOhp3seLztw8pIXsokf5etq55iX2b1pNJpdA0nepZc5hz/oVUTZ9J5bQZ7N/7CPtat2BZGUwzC2kLRQVFi2HpScyUF0UxUW1pHG4PLVs3k0klsTmco90tR236mefQs7+JPeveJhEJY3M4MbMGRjqNv7SMs6+/adgCeZ9vNnNmf5/e3pcIDryd20TPMx2Pewqh8EY6Oh7FNA00zUVx8TKqKq/D4chNH6up+SwFBQvp6X2JeGwvquakqPAsiouXH9XCdl3zoCg6pplG04b3Y266mI6mDf/9EOKj3mh/g/2R/YM7cX/Aptmo8dXQGmnl1dZXuWbaNXmMUggx0UhyIcRJRNV0VFXFyGSGPG5ZFgOd7RhGBlXXsLtcg18un4/eln1sfvkFln7iU4c9R6Svlxd//h/0t7Xi8Hiwu1xkMxka171NZ+Mult98G+VTpmIqXZhmAlV1olhqbldxS8OyQFENVGcEK+nFytpRdRMzmyWbMbAd+4ymUaNqGudcfxM1M+ew+523GOhsx+ZwUL9wMVNPX4qveORN9xyOMqqrb6C6+gYABkLradz9HdKZIHZbITbdRTYbp6Pjj4TDm5k541s4HOUoioLfP/+IdiQ/FK93Jk5nNclkKy6tZtjxdLoXt3sKHs/04zrPkbIsi/5kPxkzQ7GzGJsme3WcSN7tfhfFUoYkFh/QFA1d1Xmn8x1JLoQQQ0hyIY6bZVmkduwguuYVUk1NqE4n7jPPwHveeWjHuBmMODaarlM7ez473/wLbn/B4KLdVCxGJpVC1TSwrCFrCVRNx+n20LRxPQsvvnzEdQYH2vQ/zxBsayVQXpFrD8Dpwun1MdDVydon/sBlX/1fxGKNKIoGZFHU959nWaAoWKaKomXIZlxYGTfpRA8FpeXY3RNngaiqaUxedDqTF51+TK83zQz79z9ExggN2RxP09zotgCx2G7a2n7HlClfG72YVTvVVZ9ib9OPSCbbsdtLUVUbppkhne5GUe1UVV9/TAvdj9b6rvU8tecpdgR3YFomhY5CVkxawWWTL8Ntk5GTE0HSSB6ktHSOpmikzNQ4RiSEOBFIciGOi2VZBH/3O0KP/p5sLIZqt2Nls8TfeYfwU09T/o2/x15Xl+8wTykzzjqX5i2bCPV04S8uQdV0MukUVjaLZSm4fQXDpk3ZnC6SsSjx0MAhk4t4aIDmLZtwen0fJhbvUxQFb2Eh/W0tdO7ZTDLVgcNRQTrdg6IaKIqCZZkoKKCaYKlkEwEyqRSmYTDtzLNR1fGvxz1WwuFNJBL7cdjLh1VmUhUdm15Af/A1ajOfw2YbvvHgsSopuRDTytDW9huSqQ7Ayq3BcZRTW3MTJcXLR+1cB/PS/pd4cPODJDIJChwF6JpOb6KXX279Jdv7tnPXkrtw6vmf/iYObUrBFNZ3rR+2Pwvk3vuT2SRTC6bmKTohxEQlyYU4LvG33mLgt4+g2O3YJ00a/ANkGQbppia6f/BDqr/3XRSbTIcYLyV19Zx7w8289YdHCPf0AJBOJrAsC7fXT2FV9bAbhayRQVU1dPuh5yTFBoJkUincBSPfDNscTmLBILFQGBQVXfejax6SqXY0e5yskcGyAEPHRCXRD7FgPzWz5zF96Tmjcv0TRSrdjWVlR1z7ALkRjIwxQDrdN6rJhaIolJddSnHRMkKh9WSMEDY9QCCweFzWWgSTQX657ZdkzSy1vtrB3zWPzUPSSLKuax0vNb/E5VPyW81EHN75tefz3L7n6En0UOYuG3KsP9mPR/dwfu35eYpOCDFRSXIhjpllWYSfew4rk8FWOXQnY0XX0SsqSO3ZQ3zDu3jOPCNPUZ6aamfPo3xyA/u3bCLc3Y2ZNdj++hpAQdOH/rO3LItEJEzNrLkHXUvwAd3hQNU0shkD3Ta8gpGZNUBRcDgLKHAsoK/vFVyuSdjtxRhGhHQyRiIcI51IoOgZbEo9S65cyYyzz5sQC7kh1x+5/SUUVPXYF4Boquv99rLvTw8byrQy7y+uPrKpYNHoTrp7XmBg4B2wsnh9cygrvYiCgsUj7lmh6x6Ki5cNfm8YEWKxRlTVjtNZM6xU7Wh5s/1NBpID1PhqhsXl1J2oispLzS9x2eTLZK+NCW6SfxI3zbqJX279Jc3hZjy23I720XQUh+bg+hnXM6to1uEbEuIk8v3uXvzu0f3ANBzP8NCotphfklyIY2YlEiR37kL1jbw3gep0gmGQatwtyUUe2F1upi0564DvXbz73FPEBoK4/AWoqkrWyBDt78fh8jDn/BWHvdkLlFdSXFNH197d2F2uYc+PhUJ4AoVUTp1OIn0FA6F1pFKdOBzl2GwBbLYATneCRLIdv/c0Zl/+nWHJTr5YlkVf3xq6e54jFtuDgoLfv4Dy8sspKFh01O0VFJyG3VZEOt2HwzH0U1/Lssik+ygsXIrDUXHYtvr6XmFv048xMqFcmVlFpa9vDcHgG1RX3Uh19Q0H/dllMmHa2/+b3r6XMIwIiqLhdk2msvIaioqWjfoNfne8G+CgmxB6bB66492kzTSO49iPRIyPy6ZcRrWvmhf2vcDm3s1YlsVZVWexYtIKTi8/XRJEIcQwE+OvuhBizM278GIs02L7a2sId3eB8v46iaJSTl95DdUzDv8JpKIozPvYRfS3tRLu7cZbWIym65jZLPFwCDNrMOf8C7G73NhdC6mvv53m5p+TSO4HCyxAVW0UBk5n6tT/M6ESi+bmh+jofAzLyqLrPizLpK/vFQZC7zBp0pcoL7vsqNq02QqoqLialtbVpFLd2O3FKIqGaaZJpbvQbX4qKz952JuzVKqLpn33k80mcB2wMBxKSKf7aGv/LT7frBETIMOIsGv3twmHNqJpXmx6AMvKEoluJ7ankUmZEBUVVx7VdR2OS3dhYY04Tx8gY2bw2DzoysT42YvDW1C6gAWlCzBMA2DE6lFCCPEBeYcQx0xxuXDOmE78nXVQOHx/BDOZBF3HMVV2cJ0IVFVj4cWXM33pObTt3EY6kcATKKR61hxsh1lrcaDa2fM4+/ob2fDME0T6egcrQLl8fhZefDmzz7tg8LllpRdT4F9EX9+rJJNtqJqTAv8iCgpOG5eKRUdqYOAdOjsfR9c82GyBwcdttkJS6S6am3+O3zcfl2t4eddDqaq6DguLzs7HSCRbAVBQcbqqqav9PAUFCw/bRkf3iyRSPbhdk4bdrNvtxcTiTfT0vjhictHd/Rzh8CaczqohU7x03Usy1Ulr268oKjobu/3Q0+GOxsKyhfx+9++JZqL47ENHNU3LJJaJcdGki9BOosX7pwpJKoQQR0LeKcQxUxQF/yWXkNi4CaOvD62oaMiCbqOzE8fMmbhPO/opJWLsuAsCTDvj7ONqY8qiJdTOnkfb9q3EwyHsLjfVs+bg8g6fIudwlFFV9cnjOt9Y6+19EdPK4DwgsYD3dzW3lxFP7Kev/1Vqqm88qnYVRaOm+gbKyy5lYGAd2WwMu72UgoLFaIeZEtQV6+KpvU8R6/wlVUof0ViEQmch5e5yXPqH6zR0zUM0umPY6y3LpLvnBVTFPuLaEYe9lESyhf7+16mouOqorutQphdOZ2nlUta0rMG0TPx2P4qikDJSdMW7KHeXc1H9RaN2PiGEEBOLJBfiuLiXLqXwxhsY+O9Hyezfj/J+KVosE/uUKZT97Z1SKeokZXM4qV+4ON9hjIpYfM/gAuyPUhQVRdFIxPcdc/s2W4DS0hVH/Pz2aDur3l7FvvA+zndlQbNIZVO0RdvpS/Qxo2gGXluuZLBFFmWEKUammSSTCR60QtQHi8zT6d5juKKDUxSF2xfcjk218Wb7m7REWkDJ7YkwJTCFLy/4MrW+2lE9pxBCiIlDkgtxXBRFIXD99bgWLCC6Zg2pvU2oLhfuM5bIJnrihKGqTizLOPgTLPO4KkcdrV9v/zX7wvsocZbQmOyh2mWQNbOYKMQyMbb0bmFeyTzcuotsNklh4VnD2lAUO5rqwDCiI57DsiyA3ALxUea2ufnaaV/jmmnXsLlnMxkzQ7W3mgVlC7Cp8mGDEEKczCS5EMdNURScM2finDkz36GIE0AmnWLfxvXsWb+WWLAfl8/PlNOWMOW0JcM29xsvRYVn0xLdmdvk7yNVjkwzDYpKQWB8Rmk6Y51s6N6A3+ZnX3gfmazJPLtOqZ4llFXJYJHOpmkM7qLBG8DpKKW0ZPioiKrqFBcvp639d9hHKIdrGCE0zU1h4Zljdi21vloZpRBCiFOMJBdCiHGTTsRZ8/DPaduxDRSw2exE+nrp2tvIng1rueBzX8TtH70N5Y5USemFdPc8TyLZjNNRharm9vDIZhMkU534fLMpDAwfHRgL3fFuEkYCXdGJG3EcmpMXY3ZWeAco0QwULEzLQrWixKxS5jT874MuNC8vv5z+/tdIJJqxO8rQVDdgkskMYGSjlJddgctVPy7XNZJMKkvXvjDphIHLZ6es3oemjc3+G0IIIcaHJBdCiHGz8YWnad22BV9x8ZDdwLOZDJ17dvPOk3/g/M/81bjH5XRUMG3qN9i79wfEE82ACYCi2Cjwz6eh4a7DLsAeLQ7Nga7oBJNBFBRURSVoqvwpXEy9PUmFlsG0MsSVAorc87mo4OAjKk5nFdOn/yNN++4nFttN2uwBFHTdT2XlddTV3pyXfQosy6JxfTfbXm8nNpDCMkHVFApKXSxcUUfVtMC4xySEEGJ0SHIhhBgXyWiUvRvWYXe5hyQWAJrNhsvnp3XrFsK93fhLyg7Sytjx+WYxd+5PGBh4i1h8LwoqPt8c/P6F41o2tyHQQLWvmvZo+5DHDRQa0y62ZlXsqo8SVwmOrHXY9jyeqcyZfR+RyFaSyXZU1YbfP39Uy88erT0belj/3D6wwBtwoOkqRjrLQFecN/7UyLnXTaNi8viPYAkhhDh+klwIIcbFQFcHqVgUT2D4nigATo+Hga5O+tvb8pJcAGiag+Li8ykuPj8v54fcXgLXTL2G97rfI5QOYTftqKqKhUUmmwELytxlJLNJpgSmHFGbiqLi98/D7583xtEfXiadZdtrbWCBr8g5+Lhu1/CXOAn1JNj2Wjvl9X7Z/VkIIU5AMrlVCDEuFFUFRRmsUvRRlmnlpgGp8rZ0fs353DL3FmyqjbgRJ2kkSRpJVEWl2leNTbXh1t2cX5O/JOhYdTWFiQ6kcPvtw44pioLLZ6e3JUqkL5mH6IQQQhwvGbkQR8yyLJJbthB95VXSzftRvV48Zy7Fc845aN7RL2cpTi5FVdV4AoXEQwP4iodPyUlGIzh9fkrq6sc/uAlGURQ+N+dzODQHv9z6S5JGErfNjdfuJWkkUVD41IxPMbPoxKvQlkkaWCZo+shJpKarpBMG6eQhSgMLIYSYsCS5EEfEMk36V68m9ORTWIkEisOBZRjE33qb8LPPUP73f4+toiLfYYoJzOZwMuOs81j31J9IRiM4PN7BaS+peJxUPM68j12Ul2pRE9WnZnyKaYXTeGHfC2zr2wbA/Ir5fHzSx1lSseSEnDbk8tlRNQUjnUW3a8OOG+ksmk3D5Rs+siGEEGLim1DJxeOPP86dd95Jc3Mzc+fO5be//S2zZs0CYMuWLdx66600NjZy22238d3vfveE/MN6ooq+/DIDf3oMzeNBLSsb7HsrkyG5Ywc9P/oRlatWyc9EHNKc8y8kFuxn99o3SEQ6UFQVyzLRbDYaTj+ThZdcke8QJxRFUVhcvpjF5YvJZDNYWNi1E/umu2ySj0CZm/7OGAUlziHvGaZpkYhmmLygBE/B+G1aKIQQYvRMmMnNe/bs4dZbb+U73/kObW1tTJ8+ndtuuw2AVCrFypUrWbx4MevWrWPbtm2sXr06vwGfQizTJPTMs2BZaIHAkJsBxWbDVlpGcvsOklu25jFKcSJQNY0zP/EpLr79DuZ97CImL1zMnGUXctEXv8qyG2/BZpcbyoOxabYTPrEAUDWVhStqcbp1Qt0JUgkDI2OSjGUI9STwF7uYu6w632EKIYQ4RhNm5GL79u185zvf4frrrwfg9ttv5/LLLwfg2WefJRQK8YMf/AC3282qVav4yle+wq233prPkE8Z2WCQTHMzWsHI01UUtxurt5dUYyOueXPHOTpxolEUhbL6KZTVH1mlI3HyqZwa4Nzrp7PttXZ6WiKkEwa6TWPKwlLmnFdFQWl+dmoXQghx/CZMcnHFFUOnQ+zcuZNp06YBsGnTJpYuXYrbnfuDM3/+fLZt23bQtlKpFKlUavD7cDg8BhELIYQ4VuX1fsom+Yj0Jcmksji9NpkKJYQQJ4FxnxZ19dVXEwgEhn395Cc/GXxOOp3mvvvu48tf/jKQSw4mT548eFxRFDRNIxgMjniOe++9l4KCgsGv2trasb2ok5xWWIitro5sKDTicSseR3E4cEydOs6RCSFOZIqi4C9xUVztlcRCCCFOEuM+cvHAAw+QSCSGPV5UVDT4/3fffTcej2dwzYWu6zgcQ//wOJ1O4vE4hYXDN+T6xje+wd/+7d8Ofh8OhyXBOA6KqlJw2aV0795NdmAAtaBgyILuTE837kWLcM6dk+dIhRBCCCFEPo17clFeXn7I4y+//DL3338/b731FjabDcglHlu2bBnyvEgkgt0+8uJGh8MxLBkRx8f7sY+Rbm4m9ORTZPfvz5WizWQAcM6cSenXvy6VooQQQgghTnETZs0FQFNTEzfccAP3338/s2fPHnx8yZIlPPTQQ0Oel0qlhox2iLGlqCpFt96Ke8mS9zfRa0b1emQTPXFCSEajpJMJnF4vdqcr3+EIIYQQJ60Jk1wkEgmuuOIKrrrqKq655hqi0SgAHo+HZcuWEQ6H+cUvfsGtt97KqlWrWLFiBZo2fAMmMXYURcE1bx6uefPyHYoQR6SvtYWtr7xI6/YtmIaBzelk8sLTmbN8BZ7A8CmVp7qkkWRH/w5S2RTl7nIm+SfJiKQQQoijMmGSixdeeIFt27axbdu2YaMU9fX1/OxnP+OGG27grrvuQlVV1qxZk79ghRATXtfeRtb8v58RG+jH6fVhc7rIpJJsXvMiHY07ufCvbsdbVDxq58tmUwwMvE0ovBHTTOF21VNcvAyH49BTQScCy7J4pukZntjzBN3xbrJmFpfuYk7JHG6Zcwt1/rp8hyiEEOIEMWGSi6uuugrLsg56/Morr2TPnj2sX7+epUuXUlw8ejcFQoiTi2lmWfvE74mFBghUVA1++m53uXD5/PS1trDpf57lnE/dNCrnSyY72N14L9HoTrCy8P752jseZdKkL1FacuGonGes/HH3H/nV9l+hKRqlrlJ0VSeWibG2cy3t0XbuOfseKjwV+Q5TiGFaIi20R9uxa3ZmFM7AbZM9UoTItwmTXByJioqKwY31hBDiYLr37qG/rRVvYeGwaT2qpuHweNi/eSOLLl2J2z/y5pBHyjQzNO75V6KRrTid1ahqrpiEZZmkUp3sa/oJTkcFPt/ErKbWl+jjscbHsKt2St2lg4/77D7cNjct4Rae2vMUt82/LY9RCjFUR7SD1VtXs6lnE/FMHFVRKXGXcPnky7my4Uo0VaZNC5Ev477PhRBCjLVIsI+sYWBzOEc8bne6yKSTxAdG3ivnaIRCG4hGd+JwVA0mFgCKouJwVJIxInR1P3vc5xkr73S9QygVotg1fDRYUzS8di+vt79O0kjmITohhutN9LLq7VW83v46ds1Oja+Gcnc5oVSIX279Jb/d8dt8hyjEKU2SCyHEScfmcKIoYGazIx7PGgaaqqEfJPk4GpHIFizLQNOGt6UoCjbdR2hgHZY1ciz5FklHUBQFVRn5z4FDc5A0ksQysXGOTJyosmb2kNOcj9fzTc/TFG6ixluD3+5HVVRsmo1ydzlum5unm56mPdo+ZucXQhzaCTUtSgghjkTl1Om4/QHioYFhi7YtyyIRDlExdToFZce/2NqysnDI+ygViyyWZaIoE2+qRoG9AMuyyJrZEaeSpLIp3Lobr92bh+jEiSJpJFnTsoaXml+iK96FS3dxXvV5fHzSxyn3jF5RA8M0WNO6BrfuRleH38IUOgppjjSztnMtV0+9etTOK4Q4cjJyIYQ46TjcHmYv+xhZwyA2EMQ0TSA3YhHp7cHmdDH3go+PSplVt3syKAqmmRnxuJGN4PFMR1Vtx32usbCkYgkBZ4C+ZN+wY1kzSzQTZVnNMhyabEwqRhbPxPneO9/jp5t+SmOwEcM06E/288jOR7j7jbtpCjWN2rnS2TTxTPygv4+KoqCgEE6FR+2cQoijI8mFEOKkNHf5ChZdshJNtxHu7mKgq5NIXy/uQCFnX38jtbNHZ7+WwsKzcTqrSKY6sCxzyLFMZgBF0SkrvXhUzjUWCp2FXDf9OrJWlvZoO0kjiWEahFIhWqIt1PvruWzKZfkOU0xgT+55krWdayl1lVLtq6bIWUS5u5xaXy1t0TZ+uumnmB/5t3GsHJoDr9170DVApmViYRFwBkblfEKIoyfTooQQJyVFVVnw8UuZdubZtO3YSjqRwO0voGbWXGzO419r8QFd9zBlyp00Nv4r8cR+NM2JouhkszFUxU5lxTUUFZ03aucbC1dMuQK37uaxxsdoj7Xn9rmwuTi/5nxumnUTZe6yfIcoJqikkeSl5pdw6k6c+tB/Vx+UNt4zsIdtfduYWzL3uM+nqRofq/0YD29/GMM0hk2N6k/247P7OLPizOM+lxDi2EhyIYQ4qbn9BUw74+wxPUeBfwGzZ32Xnp7/oT/4OpaZIVBwOqWlKwgEzpzwu1wrisKFky5kWc0yGgcaSWVTVHgqZG8LcVg9iR4GUgP4bL4Rj7ttbnqTvbREWkYluQC4qP4i3up4i93B3QQcAbx2L1kzS3+qH9MyuWHmDaO6zkMIcXQkuRBCiFHgctVQV3crdXW35juUY2bTbMwqnpXvMMQJxKbaUBX1oNOeLMvCsqwRF18fq0JnId8885v8evuvWdu5ls5YJ5qqUe4uZ2XDSi6pv2TUziWEOHqSXAghhBDimJS7y6n317O9f/uIFcVCqRBeu5e5xaMzavGBUncpdyy+g+54N+3RdmyajWmBadg1+6ieRwhx9CS5EEIIIcQxURSFKxquoHGgkZ54D8WuYlRFxbIsYpkYA+kBLq2/lEpv5Zicv8xdJmuChJhgJLkQQggx6nLTYUBVJ/Z6E3H8zqk6h2AyyCM7HqEl0jL4uFNzsrxmObfOPXGnCgohjp4kF0KIcWGaWfpbW8ikUrgLAqOygZ2YeMJ9Cfa+28P+LX0YGRN/sZPJC0qZPL8EzSbVz09GiqKwsmElZ1ScwRvtb9CX7MOlu1hcvpgZhTMmfEEDIcTokuRCCDHm9m/eyHsvPUewox3TMNDtDiqnz+C0S1ZSWFmd7/DEKOltjfL673cT6U9id2iomkJPS5SelggdewY465oGdNvE26U8XyzLIp0wUFQFu/PE/3Nc7innmmnX5DsMIUSenfjvZkKICW3vu+/wxn//+v0RiwJ0m410Msn+TRsItrex4vN/TaBibOZji/GTNUzWPrWXaDBFoNSF8v50KJcPMqksLdv6Ka31MfMs+VmbWZOmTb00bugm0psEBUrrfExbUk7V1EC+wxNCiOMiY9RCiDGTSafY8MyTGJk0BWXl2J0uVE3H6fESKK8k1NPF5pdfyHeYYhR07gkR6k7gLbQPJhYfsL0/itG4oZtsdnR2aj5RmabFumf3sfbJJvrbYygaKEDrziCv/fdudq/ryneIQogJ7mtf+xqKogx+TZ06Nd8hDSHJhRBizLTv3E60rwdvYdGwedeKquLy+mjZ9h7xcChPEYrREupNYJnWQac9OVw68XCaZDQzzpFNLK07+tmzoQeHR8df7MTptuH02giUurBMi00vtRDpT+Y7TCHEBLZu3TqefvppgsEgwWCQd999N98hDSHJhRBizCSjESxA020jHtftDoxMhmQ0Mr6BiVGnaQqWlVtHMBLTzD3e2xKlfXfwlL2BbtrUi2VaOFzDZyV7AnZSMYPmrX15iEwIcSIwDIOtW7eybNkyAoEAgUAAn8+X77CGkDUXQogx43B7AMgaBpo+/O3GyKTR9Nw0KXFiK6v3Y3NopBMGDvfQZNKyINybRFHh9T/sBgtsTo2qaQEWrqjDU+DIU9Tjb6Arjs0x8uiOoiigQLjv1Ey8hDjVhcPhId87HA4cjqHvj5s3b8Y0TRYuXEhbWxvnn38+Dz74IHV1deMZ6iHJyIUQYsxUzZiFt7CIeCg47JhlWSTCYapnzMZdEBj/4MSoCpS7qZ4eIBExSCeNwREMy7ToaYmQimfQbRregANfsRNVVdi3qZe/PLLrlJoqpds1zEOsO7EsC11K9gpxSqqtraWgoGDw69577x32nG3btjFjxgwefvhh3nvvPXRd54tf/GIeoj04GbkQQowZu9PF/BWX8Paf/ptwTzfuQABNt5FJJokNBPEWFjHvYxflO0wxChRF4fTLJ5M1TNobQ8TDGcDCNHLlVr1FDgrL3YPPd3ps2Jwafe1R9rzbzZzzTo2SxHWzi3jv5RYs0xq28N1IZ1E1lUqpGCXEKamlpQW/3z/4/UdHLQA+85nP8JnPfGbw+//4j/9g8uTJhMPhIa/NJ0kuhBBjavrSc1E1jc0vv0C4tye3z4XDQcXU6Zx+xTUU10ycoVxxfBwunfOun57b16IxhJHJMtAVp313iECZa9jzNU1Ft6k0bepl9rlVp8Rma5MXlNK0qYdQbxJvoQPdpmJZFplUllgoTeWUAiobCvIdphDiIL6S+Sr29OhO5U1nosCf8Pv9R50glJWVYZomHR0dklwIIU4NiqIw7YyzmbxoCd1NjaSTSTwFAUrq6k+Jm8lTjaIqlE3yUzYp90duw/P76dwbPujPWrNppOIZzKyFpp/8vw/eQgfnfHIabz2xl1B3Ijd9zALdplI9vZAzr5yCpsu0KCHEyO666y4WLVrEjTfeCMCbb76JqqrU1tbmObIPSXIhhBgXus1G1fRZ+Q5DjDOHO/dnxrKsERMMI5PFW+hE1U7+xOIDxdVeLvnCXNobBwh1xVFUhdI6H6V1Pkm4hRCHtGDBAv7hH/6B8vJystksX/3qV7n55ptxu92Hf/E4keRCCCHEmKmZWcS219pJRjO4fPYhx7KGSTZjMWVB6Sl3U63pKrUzi6idWZTvUIQQJ5CbbrqJrVu3cu2116JpGjfddBOrVq3Kd1hDSHIhhBBizBSUupi2pJxtr7eTNVK4vDYUTSEdN0jEMpRUe5myqDTfYQohxAnj3nvvHbGS1EQhyYUQQogxNf9jtTg9Nnat7SQWSmNZFjaHzpRFpSx4/5gQQoiTgyQXQgghxpSqKsw8q5Kpi8vobYtiZi38xS68hafO5nlCCHGqkORCCCEmgGg6ytsdb9MabcWm2phfOp/ZxbNRlZOncpBu16iYLGVWhRDiZCbJhRBC5Nk7ne/wwHsP0B3vBgssLP6w+w8sKF3A10/7OgUOuSEXQghxYjh5PhITQogT0O7gbn684cf0xnup8lRR56+jzleH3+5nbedafrThR5iWme8whRBCiCMiyYUQQuTRC/tfYCA1QLW3Gl3NDSYrioLH5qHUVcqmnk1s69uW5yiFEEKIIyPJhRBC5IlhGrzd/jYu3TXicbfuJp1N817Pe+McmRBCCHFsZM2FEELkwba+bTzb9Cw7gzsB6E/2U+oqpchZNLihnKIoKChkzMwRtWlZFr2JXpJGkiJXER6bZ8ziF0IIIUYiyYUQQoyzV1tf5aebfko0HcWm2kgaSSLpCJF0hGgmSp2vDkVRMEwDFKjyVh22zc09m3ms8TG29W8ja2bx2DycV30e10y7hkJn4ThclRBCCCHTooQQYlz1Jnr52eafkTbS1PnqqPZVo6kaNtWGrur0xHsYSA1gWRadsU5KXaUsrVx6yDbf6niLe9fey9rOtdgUG16bl4SR4I+Nf+Rf3v4XgsngOF2dEEKIU50kF0IIMY5eb3udYDJIuaccRVEodhZT7ComY2YwTAPDMmiNtLI/sh+v3cuX5n8Jn9130PYSRoJfbPkF8Uw8V2XK4cdtc1PiKqHaW82O/h08uefJcbxCIYQQpzJJLoQQYhy1RloBBjfHUxWVen899QX1eG1eVEUlbaa5tP5S7j7rbs6oPOOQ7W3o2kBXrItyd/ngWo0P2FQbHt3DmtY1JIzE2FyQEEIIcQBZcyGEEOPIrtmHPaYqKqWuUkpcJXTFuih2FfOVRV85ovZ6E71YWNg024jH3TY3sXSMUCp00KpUp5qBrjg9zRFM06KgzEXZJD+qqhz+hUIIIQ5LkgshhBhH80rn8fTep0kaSZy6c+hBC5LZJOdUnXPE7bl0F5ZlYVrm4GjIgTLZDLqq49Jd9Cf7+XPzn/lL21+IpCNUeaq4oO4Czq0+d8Sk52STShisf3YfrTuCZFJZUEDTFIoqPSy5YjKFFVJdSwghjpdMixJCiHG0uGwxM4tm0hnvHDJVKWNmaI22UuIsYVHZIuKZ+BG1t6hsEX6Hf8RF25ZlMZAeYFHZIiLpCP/0+j+xeutqWiItxDIx3ut9jx9v+DH3rbuPpJEctWuciEzT4q3H9tC0sQfNplBQ6iRQ6sLpsdHTEuW1R3cTC6XyHaYQQpzwJLkQQohxZNNs/K/T/xfzS+YTTAZpDjfTHG6mLdqGZVkkjSSr3l7Fl1/8MvdvvJ/94f2HbK/UXcrF9RcTN+L0JnoxLROAVDZFa7SVImcRV0y5gv/c9J/sC++jxltDpaeSElcJtb5aipxFvNn+Jk/uPbkXfXfvC9PROIC7wIHTbRtcn2JzaPhLnIR7EzRt6s1zlEIIceKTaVFCCDHOSt2lfPucb7O5dzNbe7cSTod5o/0N+pP9mJi4NTepbIpnm55lQ9cG/m7J3zGjaMZB27th5g2oispzTc/RGm1FQUFRFOp8dXx+3udBgR39Oyh1laKp2pDXum1uHGkHL+5/kSsbrsShOcb68vOivXGAbMbE7tSGHVNVBd2msn9LL3OXVechOiGEOHlIciGEEHmgqzqLyhaxqGwRD2x6gIHUALW+WmxqbmG2Fy+FzkJaIi38fMvP+c553xlxTcUHbX1m1me4pP4SNvVsImkkKXWXsrB0ITbNxnP7niOdTePW3SO+3mf30Z/spzPWyST/pDG75nzKJHNrLA5G1VTSyez4BSSEECcpSS6EECKPQqkQr7W9hs/mG0wsPvBBFak9A3vY3r+dOcVzDtlWsauYj9V9bNjjunLot3oTExUVXT15/yR4C3MjMpZlDSvZC5BJZSmqkgXdQghxvGTNhRBC5FF3vJu4Ecdr84543G1zk8lm6Ix1HvM5ZhXPwmPzEEqHRjweSoao89dR6ak85nNMdLWzi3C4bMTD6WHHcpWjFCYvKM1DZEIIcXKR5EIIIfLIqTnRFI2MlRnxeNbMTec5nrUQ1d5qzqo6i1AqRCwTG3zcsiz6En0oisKlky896LSrk4G/2MXc86uxTAj1JEglDNJJg0h/kng4Te3sQibNLc53mEIIccI7ecfAhRDiBFDjq2FKwRS2920fcfSiP9VPoaOQeSXzjus8n5/3eWKZGOu61tGT6EFFxcTEZ/Px6Zmf5oLaC46r/RPBjKUVuPx2dr/TRbAjhmWBt9DJlEWlTD+jHE0/eZMrIYQYL5JcCCFEHimKwtXTrmZvaC8dsQ5KXaXoqo5pmQSTQVJGik9M/QQFjoLjOo/H5uHvlvwdW/q2sK5zHbFMjDJ3GWdXnU2dv26UrmZiUxSFSXOKqZtdRDyUxjQt3H67JBVCCDGKJLkQQog8W1q5lC/N/xK/3v5rOmIdWFhggdfu5drp13Lt9GtH5TyaqrGgdAELSheMSnsnKkVR8AROzpK7QgiRb5JcCCHEBHDhpAs5o/IM1naupT/Zj0f3sLh8MeWe8nyHJoQQQhwxSS6EEGKC8Nl9XFh3Yb7DEEIIIY6ZTDQVQgghhBBCjApJLoQQQgghhBCjQpILIYQQQgghxKiQ5EIIIYQQQggxKiS5EEIIIYQQQowKSS6EEEIIIYQQo0KSCyGEEEIIIcSokORCCCGEEEIIMSokuRBCCCGEEEKMCkkuhBBCCCGEEKNCkgshhBBCCCHEqJDkQgghhBBCCDEqJLkQQgghhBBCjApJLoQQQgghhBCjQs93AOPBsiwAwuFwniMRQgghhBAj+eA+7YP7tokok4idEG3m0ymRXEQiEQBqa2vzHIkQQgghhDiUSCRCQUFBvsMYwm63U1FRwe/+18oxab+iogK73T4mbY83xZrI6eEoMU2T9vZ2fD4fiqIc8evC4TC1tbW0tLTg9/vHMMJTj/Tt2JG+HTvSt2NH+nbsSN+OHenb0WVZFpFIhKqqKlR14s3cTyaTpNPpMWnbbrfjdDrHpO3xdkqMXKiqSk1NzTG/3u/3y5vGGJG+HTvSt2NH+nbsSN+OHenbsSN9O3om2ojFgZxO50mTAIyliZcWCiGEEEIIIU5IklwIIYQQQgghRoUkF4fgcDi4++67cTgc+Q7lpCN9O3akb8eO9O3Ykb4dO9K3Y0f6VojhTokF3UIIIYQQQoixJyMXQgghhBBCiFEhyYUQQgghhBBiVEhyIYQQQgghhBgVklwcxMDAAG+//TbBYDDfoQghhBBigpD7AyEOTZKLETz66KPU19dz2223UVNTw6OPPjp4bMuWLSxZsoTCwkLuuusuZD380Xn88ceZMmUKuq6zcOFCtm/fPnhM+vb49fb2MnnyZPbt2zfkcenb4yd9OLpG+l2VPj5+B3uPlb4dHQe7P5D+FeJDklx8RCgU4q//+q959dVX2bx5M/fffz933XUXAKlUipUrV7J48WLWrVvHtm3bWL16dX4DPoHs2bOHW2+9le985zu0tbUxffp0brvtNkD6djT09vZyxRVXDEsspG+Pn/Th6Brpd1X6+Pgd7D1W+nZ0HOz+QPpXiI+wxBDNzc3Wr371q8HvN23aZHm9XsuyLOtPf/qTVVhYaMViMcuyLGvjxo3WOeeck5c4T0RPPvmk9cADDwx+//LLL1sul8uyLOnb0XDhhRdaP/rRjyzAampqGnxc+vb4SR+OrpF+V6WPj9/B3mOlb0fHwe4PpH+FGErPd3Iz0dTW1vKZz3wGgEwmww9/+EOuueYaADZt2sTSpUtxu90AzJ8/n23btuUt1hPNFVdcMeT7nTt3Mm3aNED6djQ89NBDTJ48ma9//etDHpe+PX7Sh6NrpN9V6ePjd7D3WOnb0XGw+wPpXyGGOmWnRV199dUEAoFhXz/5yU+A3B+6iooKnnvuOX784x8DEA6HmTx58mAbiqKgaZos6vqIw/UtQDqd5r777uPLX/4yIH17pA7Vtwf234Gkb4+f9OHoGul3Vfp4dB34Hit9O7o+en8g/SvEUKfsyMUDDzxAIpEY9nhRURGQ++ThhRde4M477+S2227j97//Pbqu43A4hjzf6XQSj8cpLCwcl7hPBIfrW4C7774bj8czuOZC+vbIHEnffpT07fGTPhx70sej68D32H/4h3+Qvh1FH70/aGhokP4V4gCnbHJRXl5+yOOKorB48WJ++ctf0tDQwMDAAEVFRWzZsmXI8yKRCHa7fSxDPeEcrm9ffvll7r//ft566y1sNhuA9O0ROlzfjkT69vhJH4496ePR89H3WOnb0fXR+4N7771X+leIA5yy06IO5pVXXhmsDgVgt9tRFAVVVVmyZAlvvvnm4LGmpiZSqdQhPzUWQzU1NXHDDTdw//33M3v27MHHpW/HjvTt8ZM+HHvSx6NjpPdY6dvRcbD7g1mzZkn/CnEASS4+Yvr06Tz44IM8+OCDtLS08M1vfpOLLroIv9/PsmXLCIfD/OIXvwBg1apVrFixAk3T8hz1iSGRSHDFFVdw1VVXcc011xCNRolGo1iWJX07hqRvj5/04diTPj5+B3uPPe+886RvR8HB7g8uu+wy6V8hDpTvclUT0QsvvGDNnj3b8vl81ic/+Umru7t78Njjjz9uud1uq7i42CotLbW2bt2ax0hPLI899pgFDPv6oBSl9O3o4COlaC1L+nY0SB+Ovo/+rkofH59DvcdK346Og90fSP8K8SHFsmQbyaPV2dnJ+vXrWbp0KcXFxfkO56QifTt2pG+Pn/Th2JM+HjvSt2NL+leIHEkuhBBCCCGEEKNC1lwIIYQQQgghRoUkF0IIIYQQQohRIcmFEEIIIYQQYlRIciGEEEIIIYQYFZJcCCGEEEIIIUaFJBdCiDG1evVqFEUZ9vXiiy8OPmfNmjXU19fnL0jgnnvuGYzN4/GwYsUKGhsbj/j1q1evZvny5aMa00Rp89133+W0007D5/Nx7bXXEgwGRzUmIYQQJw9JLoQQY27u3LkEg8EhXwfe4J577rm89957o3pORVHYt2/fUb3msssuIxgMsnXrVhoaGrj++uuP+LU33ngjTz311FFGOf5tHq1IJMJll13GypUree+991BVla9//et5jWm0TYTkVgghThZ6vgMQQpz8NE0jEAgc9Liu6/j9/vEL6CBsNhuBQIBAIMCqVasoKSmho6ODysrKw77Wbrdjt9tHNZ6xaPNo/e53v6OoqIhvfetbAHzzm99k6dKlrF69GlWVz6eEEEIMJX8ZhBB5d7BPjpcvX87q1av5wQ9+wKRJk3jmmWcGj/32t79l8uTJeDweLr74Ynp7ewGYOXMmiqIAMHnyZBRF4ZFHHjnqmHQ999lLOp0GoL6+nhdffJFvfOMbVFRUsHXr1iHPH2m60T333MMtt9zCt7/9bQKBAJMnT+b1118fcg3Tpk2joKCAT3/604RCocO2ecstt/DpT3+axYsXU1RUxFe+8hUymczg8Yceeoi6ujp8Ph+f+MQniEajR33tB3rzzTc5++yzB7+fOnUqt956K7FYDID777+f+vp6KisrueeeezBNk3379qEoCnfeeSder5d///d/p6GhgdmzZxONRlm+fDlf+MIXmDlzJmVlZdxzzz1DzjlSmx9c+z/+4z/yla98Ba/Xy9y5c9m5cycAlmXxve99j0mTJlFZWcmPfvSjwfaWL1/O/fffz3XXXYfH4+Hss8+mq6uLzs5OFEXhggsuYP/+/YPT4jo7O4+rz4QQ4lQmyYUQYsxt3rx5cEQgEAiwefPmI37tAw88wP/8z//wwAMPcOaZZwK5qTqf+9znuPfee9m6dSu6rnPfffcB8M477wyuCdi0aRPBYJBrr732qOI1DIMf//jHTJ48mbq6usHH//Ef/5HW1lZ+/etfH/E0mmeeeYa9e/fy7rvvcs455/CNb3wDgDfeeIPbbruN++67j40bN9LR0cHdd999RG0+8cQT/N//+39Zs2YNzz//PD/5yU8A2Lp1K7fffjs/+9nP2LZtG319ffzHf/zHUV37R7W3t1NWVjb4vc/n4z//8z/x+Xz84Q9/4Fvf+harV6/m6aef5te//jU//vGPB5/b0NDAddddx7e//W0ee+wxWltb2bRpEwCPP/44q1ev5o9//CM/+clP+NOf/gRw2DYfeOAB/H4/W7ZsoaysjH/5l38B4OGHH2bVqlX89re/5dFHH+Wb3/wmr7322uDr/vmf/5nzzjuPTZs2EQqFuP/++ykvLycYDPLkk09SW1s7OGWvvLz8uPpMCCFOZTItSggx5mbMmDFk1KGqquqIXxuNRnnllVeGTA/SdR1d10mn01RWVvLEE08Mfrrt8/kGn+f3+w85Heujnn76aQKBALFYjEmTJvGb3/xmcBQEoKCggIcffviI2/sg1gceeACHw8HNN9/Ml770JSA3KnHTTTdx5ZVXAvDggw/S3t5+RG1+4hOf4NJLLwXgK1/5Co8//jh33nknDQ0NdHZ24nQ6efvtt8lkMoOf7B+rTCaDpmkjHnvwwQe54447BkdX7rnnHv75n/+Zq6++GoDbbruN3t5eLrzwQubNm0dRUdHgKMsXv/hFli5dCsBnPvMZHn/8ca655pqDtnnHHXcAUFtby7333gvApz/9aX77298CueTii1/84uAoy8qVK3niiSc499xzATj77LP52te+BsBVV11FS0sLiqIQCATwer2oqnpUvytCCCFGJiMXQogxZ7fbqa+vH/w6mnUEX/7yl4c93+Vy8cgjj/Dggw9SVlbGlVdeSUtLy3HHecEFF7Bx40ba29tpbGwcvPn9wFe/+tWjbnPp0qU4HA4g1w+WZQHQ0tLClClTBp83Y8YMLrjggiNqs7a2dvD/q6ur6erqAiCRSPCFL3yB+vp6Vq1ahaZpZLPZo475QIFAgIGBgcHve3p60HWdjo6OYdfQ0NBAc3Pz4PdOp3PIf4/kGg7X5vnnnz/4/wf2Z2trK//+7/8+ODr22GOPDXndgdPLDnydEEKI0SXJhRBiQvN4PMMe6+/vp7y8nNdee42uri5KSkoGP9n+gKIoR30D6Xa7qa+vp7S09IhjOZyDLVSvra0dUs3qz3/+8+BoxOEc+LqWlhYqKioA+NGPfkQoFKKjo4OXXnqJs84666jj/aiFCxeybt26we/37t2LruuUlpZSV1fH3r17hxybNGnScV3D4do8WH/W1NTwrW99i40bN7Jx40Y2bdo0OMJxqNcBqKoqyYYQQowSSS6EECec7u5uli9fznPPPUd/fz+QWydxoIaGBp599lna2tp49dVX8xHmId166608/PDDPPXUUzQ1NXHvvfcOWd9xKI899hjPPPMM7733Hvfff//gmpJIJIJpmvT09PCb3/yGn/70p8d903zzzTfz3nvv8cMf/pC9e/fyD//wD1x99dXous4XvvAF/u3f/o1XXnmFd999l7vvvpsvf/nLR9Tuz372M958801ee+01fvOb3wxew7G2efPNN/PII48QiUSwLIsvfvGL3H///UcUy5QpU2hvb+fdd9+lsbGRd99994heJ4QQYjhZcyGEOOHMnDmT++67j9tvv53Ozk4WLFjAz3/+8yHP+elPf8qXvvQl7rjjDq677jqWLVuWp2hHdtZZZ/HQQw9x55130tvby8qVK/ne9753RK+97rrr+D//5//Q0tLCZz/72cF1HHfccQfXX38906dP56yzzuLzn/88f/7zn48rzrq6Op566in+5m/+hn/6p3/ikksu4ac//SkA1157LR0dHdx8882k02m+9KUv8dWvfnXIdKSDuf7667nxxhuJRqPccccdXHHFFYds83BuuukmOjo6uPzyywmHw1x99dV8+9vfPqJrrKmp4d5772XFihUkk0m++93vsmjRoiN6rRBCiKEUS8aChRDihHHLLbdQX18/rHzriWT58uXccsst3HLLLfkORQghxCiTaVFCCCGEEEKIUSEjF0IIIYQQQohRISMXQgghhBBCiFEhyYUQQgghhBBiVEhyIYQQQgghhBgVklwIIYQQQgghRoUkF0IIIYQQQohRIcmFEEIIIYQQYlRIciGEEEIIIYQYFZJcCCGEEEIIIUbF/w9I9miLXBwFLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "创建额外特征...\n",
      "特征工程后特征数量: 1030\n"
     ]
    }
   ],
   "source": [
    "# 2. 特征工程和数据预处理\n",
    "print(\"\\n=== 步骤2：特征工程和数据预处理 ===\")\n",
    "\n",
    "# 2.1 特征标准化\n",
    "print(\"特征标准化...\")\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 2.2 PCA降维（可选，用于可视化）\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "print(\"应用PCA降维...\")\n",
    "pca = PCA(n_components=50)  # 降到50维\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "print(f\"原始特征维度: {X.shape[1]}\")\n",
    "print(f\"PCA后特征维度: {X_pca.shape[1]}\")\n",
    "print(f\"PCA解释方差比: {pca.explained_variance_ratio_.sum():.4f}\")\n",
    "\n",
    "# 可视化PCA结果\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='tab10', alpha=0.7)\n",
    "plt.colorbar(label='Person ID')\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "plt.title('PCA Visualization of Face Data')\n",
    "plt.show()\n",
    "\n",
    "# 2.3 创建额外特征\n",
    "print(\"创建额外特征...\")\n",
    "\n",
    "# 计算图片的统计特征\n",
    "X_features = X.copy()\n",
    "\n",
    "# 像素统计特征\n",
    "X_features['pixel_mean'] = X.mean(axis=1)\n",
    "X_features['pixel_std'] = X.std(axis=1)\n",
    "X_features['pixel_max'] = X.max(axis=1)\n",
    "X_features['pixel_min'] = X.min(axis=1)\n",
    "X_features['pixel_range'] = X_features['pixel_max'] - X_features['pixel_min']\n",
    "\n",
    "# 对称性特征（左右对称）\n",
    "X_features['symmetry'] = 0\n",
    "for i in range(len(X)):\n",
    "    img_reshaped = X.iloc[i].values.reshape(32, 32)\n",
    "    left_half = img_reshaped[:, :16]\n",
    "    right_half = np.fliplr(img_reshaped[:, 16:])\n",
    "    symmetry = np.corrcoef(left_half.flatten(), right_half.flatten())[0, 1]\n",
    "    X_features.loc[i, 'symmetry'] = symmetry if not np.isnan(symmetry) else 0\n",
    "\n",
    "print(f\"特征工程后特征数量: {X_features.shape[1]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6dbc6ef-a18a-4535-ac5c-b16e3b114441",
   "metadata": {},
   "source": [
    "## 对数据集划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a220023-263b-4e28-86b6-6a2965baab07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤3：数据集划分 ===\n",
      "训练集大小: (320, 1030)\n",
      "测试集大小: (80, 1030)\n",
      "\n",
      "训练集标签分布:\n",
      "1     8\n",
      "2     8\n",
      "3     8\n",
      "4     8\n",
      "5     8\n",
      "6     8\n",
      "7     8\n",
      "8     8\n",
      "9     8\n",
      "10    8\n",
      "11    8\n",
      "12    8\n",
      "13    8\n",
      "14    8\n",
      "15    8\n",
      "16    8\n",
      "17    8\n",
      "18    8\n",
      "19    8\n",
      "20    8\n",
      "21    8\n",
      "22    8\n",
      "23    8\n",
      "24    8\n",
      "25    8\n",
      "26    8\n",
      "27    8\n",
      "28    8\n",
      "29    8\n",
      "30    8\n",
      "31    8\n",
      "32    8\n",
      "33    8\n",
      "34    8\n",
      "35    8\n",
      "36    8\n",
      "37    8\n",
      "38    8\n",
      "39    8\n",
      "40    8\n",
      "Name: count, dtype: int64\n",
      "\n",
      "测试集标签分布:\n",
      "1     2\n",
      "2     2\n",
      "3     2\n",
      "4     2\n",
      "5     2\n",
      "6     2\n",
      "7     2\n",
      "8     2\n",
      "9     2\n",
      "10    2\n",
      "11    2\n",
      "12    2\n",
      "13    2\n",
      "14    2\n",
      "15    2\n",
      "16    2\n",
      "17    2\n",
      "18    2\n",
      "19    2\n",
      "20    2\n",
      "21    2\n",
      "22    2\n",
      "23    2\n",
      "24    2\n",
      "25    2\n",
      "26    2\n",
      "27    2\n",
      "28    2\n",
      "29    2\n",
      "30    2\n",
      "31    2\n",
      "32    2\n",
      "33    2\n",
      "34    2\n",
      "35    2\n",
      "36    2\n",
      "37    2\n",
      "38    2\n",
      "39    2\n",
      "40    2\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 22\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# 标准化特征\u001b[39;00m\n\u001b[0;32m     21\u001b[0m scaler_final \u001b[38;5;241m=\u001b[39m StandardScaler()\n\u001b[1;32m---> 22\u001b[0m X_train_scaled \u001b[38;5;241m=\u001b[39m scaler_final\u001b[38;5;241m.\u001b[39mfit_transform(X_train)\n\u001b[0;32m     23\u001b[0m X_test_scaled \u001b[38;5;241m=\u001b[39m scaler_final\u001b[38;5;241m.\u001b[39mtransform(X_test)\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:295\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    293\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 295\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    298\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    299\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    300\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    301\u001b[0m         )\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1098\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m   1083\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   1084\u001b[0m             (\n\u001b[0;32m   1085\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis object (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) has a `transform`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1093\u001b[0m             \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[0;32m   1094\u001b[0m         )\n\u001b[0;32m   1096\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1097\u001b[0m     \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[1;32m-> 1098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[0;32m   1099\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1100\u001b[0m     \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[0;32m   1101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py:876\u001b[0m, in \u001b[0;36mStandardScaler.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    874\u001b[0m \u001b[38;5;66;03m# Reset internal state before fitting\u001b[39;00m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[1;32m--> 876\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpartial_fit(X, y, sample_weight)\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1472\u001b[0m     )\n\u001b[0;32m   1473\u001b[0m ):\n\u001b[1;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py:912\u001b[0m, in \u001b[0;36mStandardScaler.partial_fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    880\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Online computation of mean and std on X for later scaling.\u001b[39;00m\n\u001b[0;32m    881\u001b[0m \n\u001b[0;32m    882\u001b[0m \u001b[38;5;124;03mAll of X is processed as a single batch. This is intended for cases\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    909\u001b[0m \u001b[38;5;124;03m    Fitted scaler.\u001b[39;00m\n\u001b[0;32m    910\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    911\u001b[0m first_call \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples_seen_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 912\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[0;32m    913\u001b[0m     X,\n\u001b[0;32m    914\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    915\u001b[0m     dtype\u001b[38;5;241m=\u001b[39mFLOAT_DTYPES,\n\u001b[0;32m    916\u001b[0m     force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    917\u001b[0m     reset\u001b[38;5;241m=\u001b[39mfirst_call,\n\u001b[0;32m    918\u001b[0m )\n\u001b[0;32m    919\u001b[0m n_features \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    921\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:608\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    537\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m    538\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    539\u001b[0m     X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m    545\u001b[0m ):\n\u001b[0;32m    546\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m    547\u001b[0m \n\u001b[0;32m    548\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 608\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_feature_names(X, reset\u001b[38;5;241m=\u001b[39mreset)\n\u001b[0;32m    610\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m    611\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    612\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    613\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    614\u001b[0m         )\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:469\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Set or check the `feature_names_in_` attribute.\u001b[39;00m\n\u001b[0;32m    450\u001b[0m \n\u001b[0;32m    451\u001b[0m \u001b[38;5;124;03m.. versionadded:: 1.0\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    465\u001b[0m \u001b[38;5;124;03m       should set `reset=False`.\u001b[39;00m\n\u001b[0;32m    466\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reset:\n\u001b[1;32m--> 469\u001b[0m     feature_names_in \u001b[38;5;241m=\u001b[39m _get_feature_names(X)\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m feature_names_in \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    471\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names_in_ \u001b[38;5;241m=\u001b[39m feature_names_in\n",
      "File \u001b[1;32mD:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:2229\u001b[0m, in \u001b[0;36m_get_feature_names\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m   2227\u001b[0m \u001b[38;5;66;03m# mixed type of string and non-string is not supported\u001b[39;00m\n\u001b[0;32m   2228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m types:\n\u001b[1;32m-> 2229\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   2230\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names are only supported if all input features have string names, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2231\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut your input has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtypes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m as feature name / column name types. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2232\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you want feature names to be stored and validated, you must convert \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2233\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthem all to strings, by using X.columns = X.columns.astype(str) for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2234\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexample. Otherwise you can remove feature / column names from your input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2235\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, or convert them all to a non-string data type.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2236\u001b[0m     )\n\u001b[0;32m   2238\u001b[0m \u001b[38;5;66;03m# Only feature names of all strings are supported\u001b[39;00m\n\u001b[0;32m   2239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m types[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[1;31mTypeError\u001b[0m: Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type."
     ]
    }
   ],
   "source": [
    "# 3. 数据集划分\n",
    "print(\"\\n=== 步骤3：数据集划分 ===\")\n",
    "\n",
    "# 使用原始特征\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_features, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "print(f\"训练集大小: {X_train.shape}\")\n",
    "print(f\"测试集大小: {X_test.shape}\")\n",
    "\n",
    "print(f\"\\n训练集标签分布:\")\n",
    "train_label_counts = y_train.value_counts().sort_index()\n",
    "print(train_label_counts)\n",
    "\n",
    "print(f\"\\n测试集标签分布:\")\n",
    "test_label_counts = y_test.value_counts().sort_index()\n",
    "print(test_label_counts)\n",
    "\n",
    "# 标准化特征\n",
    "scaler_final = StandardScaler()\n",
    "X_train_scaled = scaler_final.fit_transform(X_train)\n",
    "X_test_scaled = scaler_final.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690e927a-0495-4f99-ac55-cc6e366602e8",
   "metadata": {},
   "source": [
    "## 多模型训练和对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9147d72-5255-4687-a54a-552186bd84c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤4：多模型训练和对比 ===\n",
      "开始训练模型...\n",
      "\n",
      "训练 Random Forest...\n",
      "训练 Random Forest 时出错: Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.\n",
      "\n",
      "训练 SVM...\n",
      "训练 SVM 时出错: name 'X_train_scaled' is not defined\n",
      "\n",
      "训练 KNN...\n",
      "训练 KNN 时出错: name 'X_train_scaled' is not defined\n",
      "\n",
      "训练 Logistic Regression...\n",
      "训练 Logistic Regression 时出错: name 'X_train_scaled' is not defined\n",
      "\n",
      "训练 XGBoost...\n",
      "训练 XGBoost 时出错: Invalid classes inferred from unique values of `y`.  Expected: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39], got [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40]\n",
      "\n",
      "训练 LightGBM...\n",
      "LightGBM 性能指标:\n",
      "  准确率: 0.9125\n",
      "  精确率(加权): 0.9125\n",
      "  召回率(加权): 0.9125\n",
      "  F1分数(加权): 0.8992\n",
      "  AUC(宏平均): 0.9881\n"
     ]
    }
   ],
   "source": [
    "# 4. 模型训练和搭建\n",
    "print(\"\\n=== 步骤4：多模型训练和对比 ===\")\n",
    "\n",
    "# 初始化多个分类模型\n",
    "models = {\n",
    "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "    'SVM': SVC(kernel='rbf', probability=True, random_state=42),\n",
    "    'KNN': KNeighborsClassifier(n_neighbors=3),\n",
    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=1000),\n",
    "    'XGBoost': xgb.XGBClassifier(random_state=42, eval_metric='mlogloss', use_label_encoder=False),\n",
    "    'LightGBM': LGBMClassifier(random_state=42, verbosity=-1)\n",
    "}\n",
    "\n",
    "# 训练和评估模型\n",
    "model_results = {}\n",
    "predictions = {}\n",
    "\n",
    "print(\"开始训练模型...\")\n",
    "for name, model in models.items():\n",
    "    print(f\"\\n训练 {name}...\")\n",
    "    \n",
    "    try:\n",
    "        # 训练模型\n",
    "        if name in ['Random Forest', 'XGBoost', 'LightGBM']:\n",
    "            # 树模型不需要标准化\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred = model.predict(X_test)\n",
    "            y_pred_proba = model.predict_proba(X_test)\n",
    "        else:\n",
    "            # 其他模型使用标准化数据\n",
    "            model.fit(X_train_scaled, y_train)\n",
    "            y_pred = model.predict(X_test_scaled)\n",
    "            y_pred_proba = model.predict_proba(X_test_scaled)\n",
    "        \n",
    "        # 计算评估指标\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred, average='weighted', zero_division=0)\n",
    "        recall = recall_score(y_test, y_pred, average='weighted', zero_division=0)\n",
    "        f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)\n",
    "        \n",
    "        # 对于多分类，计算宏平均AUC\n",
    "        try:\n",
    "            from sklearn.preprocessing import label_binarize\n",
    "            y_test_bin = label_binarize(y_test, classes=sorted(y.unique()))\n",
    "            auc = roc_auc_score(y_test_bin, y_pred_proba, average='macro', multi_class='ovr')\n",
    "        except:\n",
    "            auc = 0.0\n",
    "        \n",
    "        # 保存结果\n",
    "        model_results[name] = {\n",
    "            'model': model,\n",
    "            'y_pred': y_pred,\n",
    "            'y_pred_proba': y_pred_proba,\n",
    "            'accuracy': accuracy,\n",
    "            'precision': precision,\n",
    "            'recall': recall,\n",
    "            'f1': f1,\n",
    "            'auc': auc\n",
    "        }\n",
    "        \n",
    "        predictions[name] = {\n",
    "            'y_pred': y_pred,\n",
    "            'y_pred_proba': y_pred_proba\n",
    "        }\n",
    "        \n",
    "        # 打印结果\n",
    "        print(f\"{name} 性能指标:\")\n",
    "        print(f\"  准确率: {accuracy:.4f}\")\n",
    "        print(f\"  精确率(加权): {precision:.4f}\")\n",
    "        print(f\"  召回率(加权): {recall:.4f}\")\n",
    "        print(f\"  F1分数(加权): {f1:.4f}\")\n",
    "        print(f\"  AUC(宏平均): {auc:.4f}\")\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"训练 {name} 时出错: {e}\")\n",
    "        continue\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff55119c-4e21-4de3-a63b-ca7533d44d40",
   "metadata": {},
   "source": [
    "## 性能可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b68a5b4-24f3-45b9-8812-0cc7e45fadd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤5：模型性能可视化对比 ===\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. 模型性能可视化对比\n",
    "print(\"\\n=== 步骤5：模型性能可视化对比 ===\")\n",
    "\n",
    "if len(model_results) == 0:\n",
    "    print(\"没有成功训练的模型，请检查数据\")\n",
    "else:\n",
    "    # 5.1 性能指标对比图\n",
    "    plt.figure(figsize=(16, 10))\n",
    "    \n",
    "    model_names = list(model_results.keys())\n",
    "    colors = ['lightblue', 'lightgreen', 'orange', 'pink', 'lightyellow', 'lightcoral'][:len(model_names)]\n",
    "    \n",
    "    # 准确率对比\n",
    "    plt.subplot(2, 3, 1)\n",
    "    accuracy_scores = [model_results[name]['accuracy'] for name in model_names]\n",
    "    bars = plt.bar(model_names, accuracy_scores, color=colors)\n",
    "    plt.title('各模型准确率对比', fontsize=12)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, score in zip(bars, accuracy_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                 f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "    \n",
    "    # 精确率对比\n",
    "    plt.subplot(2, 3, 2)\n",
    "    precision_scores = [model_results[name]['precision'] for name in model_names]\n",
    "    bars = plt.bar(model_names, precision_scores, color=colors)\n",
    "    plt.title('各模型精确率对比(加权)', fontsize=12)\n",
    "    plt.ylabel('Precision')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, score in zip(bars, precision_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                 f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "    \n",
    "    # 召回率对比\n",
    "    plt.subplot(2, 3, 3)\n",
    "    recall_scores = [model_results[name]['recall'] for name in model_names]\n",
    "    bars = plt.bar(model_names, recall_scores, color=colors)\n",
    "    plt.title('各模型召回率对比(加权)', fontsize=12)\n",
    "    plt.ylabel('Recall')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, score in zip(bars, recall_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                 f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "    \n",
    "    # F1分数对比\n",
    "    plt.subplot(2, 3, 4)\n",
    "    f1_scores = [model_results[name]['f1'] for name in model_names]\n",
    "    bars = plt.bar(model_names, f1_scores, color=colors)\n",
    "    plt.title('各模型F1分数对比(加权)', fontsize=12)\n",
    "    plt.ylabel('F1 Score')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, score in zip(bars, f1_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                 f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "    \n",
    "    # AUC对比\n",
    "    plt.subplot(2, 3, 5)\n",
    "    auc_scores = [model_results[name]['auc'] for name in model_names]\n",
    "    bars = plt.bar(model_names, auc_scores, color=colors)\n",
    "    plt.title('各模型AUC对比(宏平均)', fontsize=12)\n",
    "    plt.ylabel('AUC Score')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, score in zip(bars, auc_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                 f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01a160c-49c1-4d7a-924d-ece5b4f7b9e4",
   "metadata": {},
   "source": [
    "## 混淆矩阵和特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d95244c-8da1-45c0-b28c-43e1b2635ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤6：混淆矩阵和特征重要性分析 ===\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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36TWQLvVS/Po39NeqSZMmOnLkiKRLXzbr16/v8qC74jRt2lSSnNOlSpe+XDscDq1fv14JCQnXFDA2btyoe+65R82bN9ctt9yiwsJC55fxbdu2ydvb2yUk9OvXT61atdJLL73kDFMzZ85U06ZNNWTIkKs+b0kuXLiguXPnau7cuS7tISEhRfb7tW+//VZeXl7OW6feeustTZw4UWvWrHHe9rRhwwb1799fW7ZsUXh4uDMgVKtWrdSaunbtqtzcXEnSzp07FR0drTNnzlzxuCtp166dNm3adE0Bo7QxJr/e9tv9CgsLS73t6Vqkp6drz549uuuuu1zaz507V+S/y9dff+2WWbAA/H6MwQAAk7399tsKDw/X2bNndfjwYaWkpLiMjZB0Vbd+XP4SWlzPRufOnbVx40aXZ0Ps2rWr1NdLSUmRJJffClerVk1NmjSRpGu+RapTp05au3atcyDzr3l6eio2NlYff/xxkeOsVqvzy3z79u1Vp06dazpvcRwOh7744guXcR9vv/22GjZs6DIOJD8/X48//rjLzzQ4OFhBQUEKCAjQqlWrNGbMGDVv3lwHDx5UZmamsrOzNWLECI0bN07t2rVTQECAatWqpYYNG5brNMWl6devnz744INrOuaWW26RxWJxPhtFko4cOaLjx4+73L63adMm598zMzN18OBBZzj9vbZv364ePXooLS3N2fbdd9/p8OHDiomJcdn3gw8+0EMPPWTKeQFULAIGABRj+/btWr9+vcvy69/8l+byfeZr1qxRWlqaDhw4oKysrGuuoUWLFqpZs6Zee+01bd26VW+99ZZzxp/nnntO3t7e6tGjh9auXatXXnnF5YujdOmWoZ07d2rbtm1699139dxzzyk0NFTt27d32a9169aqU6dOmW5H6dq1a7HPrPD09FTdunWLjIcoL+PGjVNAQIDLcnkWqd+2+/v7OwfW/1b37t21atUqxcbGasmSJWrRooUiIiKUn5+vDh06XPO4h/LSu3dvZWRkFBm0X5rGjRtrwIABev755/Xuu+9q9erV6tu3ryIiIlyexbF161YlJCRo/fr1euyxx2S1Wp1jNn6ve++9VzfeeKO6deumuXPnatKkSerVq5caN27sMkva0qVLlZWVpZ49e5pyXgAVi1ukAKAYxc2ONG3aNP3lL3+54rH9+vXT2LFj9eyzzyo9Pd05IPv555/X5MmTr7oGPz8/ffjhhxo5cqRefvllNWrUSH379pV0aarTjRs36qmnnlLv3r3VunVrDR06VIcOHXIen52drejoaFksFtWvX1/33nuvXnnllSKzKLVu3Vrnzp276rqu1rBhw7Ro0SLl5eVdcSBzQUGBCgoKdOjQIeXm5io7O1teXl46ePCgcwajy+NLLl68qLy8PIWGhqpWrVo6deqURowYoYSEBJewU9JzMC7f3nT+/Pkiz6GoXbu27r33XtlsNv3www/avn27hg4dqtOnTys2NlY2m03dunXTQw89pK5duxa5rss9JZKUkZGhnJwcVatWTR4eHs5B+5mZmbpw4YLy8vLkcDiK3MJ1NTw9PfX222/roYceUtu2bZ29UFcyf/581a1bVy+88IJycnLUo0cPzZo1y+WWrb/85S/avXu3Zs6cqWbNmmnlypWqW7fuNddYHC8vL33xxRd65plnNHLkSNWsWVN9+/bV+PHjnYPxDx8+rGeffVbLli1jkDdQRVmMa5n4GwBwRR06dFDTpk01ePBgVatWTVlZWVqwYIHWrl1bZCrOP4pPPvlECQkJxQ6W/vbbb7VlyxbNmDFDzz33nBISEorsExsbq1q1aumXX37Rxo0bnQ++K87lJ3S///77euSRR9SmTRv997//dX6RvxLDMJSXl6f/+7//0xtvvKFTp05pwIABys7OVkZGho4ePap69eopLi5OzzzzjBo1aiTp0m1Ya9eu1dy5c/Xll1+qffv2zlove/zxx1VQUKAlS5bo5Zdf1rhx40qtpXbt2i63C12refPm6cMPP3TOHPZ7hYaG6s9//rNGjRplyuuVxd133624uDg99dRTbqsBwO9DDwYAmOyFF17QhAkT1KtXL2VnZ8vf31/t2rUrdjzCH4XD4ZDD4Sh2m8Vi0fjx49WxY0c9/vjjxe6Tn5+vvLy8Em9dKk1xs1VdiwYNGqhnz55KT09X06ZN1a5dO0VERBQJODabTb1791bv3r2VmpoqLy+vIk/0zs/Pd47vGD16tF544QXZbLZig09eXl6RB/Zdq6FDh6pfv36/6zUqm+XLl5f5CecAKgd6MAAA5eryPzMl9UgAAP5Y6MEAAJQrggUAXF+YRQoAAACAaQgYAAAAAExDwAAAAABgGsZgVCKFhYU6deqUatasyT3LAAAAqFQMw1BWVpYaNGhQ6rTgBIxK5NSpU2V64BIAAABQUU6ePKng4OAStxMwKpHLTzE9efKk/Pz83FwNAAAA8D92u10hISHO76wlIWBUIpdvi/Lz8yNgAAAAoFK60q38BIxKqOOYJbLafNxdBgAAACqRpCkD3F3CVWEWKQAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATEPAAAAAAGAaAgYAAAAA0xAwyiAjI0Pbt29Xenq6u0sBAAAAKhUCRgkWLlyoVq1aKSAgQHFxcUpLS5MkLV26VKGhoRoyZIiCg4O1dOlS5zErVqxQ48aN5enpqbZt2+rAgQPuKh8AAABwCwJGMdavX6/4+HhNmzZNe/bskd1uV9++fZWZmamnn35amzdv1t69ezV79mwlJCRIko4cOaLBgwdr0qRJ+vHHH9WsWTMNGTLEzVcCAAAAVCye5F2MRYsWadCgQercubMkacqUKWrZsqUyMjI0ffp0RUZGSpKioqJ07tw5SdKBAwc0adIkPfzww5Kk4cOHq0ePHu65AAAAAMBNCBjFSEtLU+vWrZ3rVqtVkhQQEKDHHntMkpSfn69p06apb9++kqSePXu6vMahQ4fUtGnTUs/jcDjkcDic63a73ZT6AQAAAHfhFqliREVFafXq1SosLJQkJSYmKjo6Wv7+/pKk5ORk1atXT2vXrtXMmTOLHJ+Xl6epU6dq2LBhpZ5n4sSJ8vf3dy4hISHmXwwAAABQgQgYxRg1apQKCwsVFRWl9u3ba9KkSRoxYoRze2RkpNatW6emTZsWO85i7Nixql69+hXHYIwePVqZmZnO5eTJk6ZfCwAAAFCRuEWqGAEBAfrmm290+PBhvf7668rIyFD//v2d2y0Wi9q1a6f33ntP4eHhysjIUEBAgCRpw4YNmj17trZt2yYvL69Sz2Oz2WSz2crzUgAAAIAKRQ9GKRo0aKDly5dr4sSJslqt2rRpk3PWKEny9vaWxWKRh8elH2Nqaqri4uI0e/ZstWjRwl1lAwAAAG5DD0YpZs2apYiICPXp00eS1KxZM82bN09NmzZVt27dNGbMGHXp0kV+fn7Kzc1Vz5491bt3b/Xt21fZ2dmSpOrVq8tisbjxKgAAAICKQw9GCdLT0zV58mRNnTrV2Va/fn0tW7ZMM2bMUMuWLZWTk6NFixZJktatW6eUlBTNnz9fNWvWdC7Hjx931yUAAAAAFY4ejBIEBgY6n3Hxa507d9b+/fuLtPfu3VuGYVREaQAAAEClRQ8GAAAAANMQMAAAAACYhoABAAAAwDQEDAAAAACmIWAAAAAAMA2zSFVCm8fHyc/Pz91lAAAAANeMHgwAAAAApiFgAAAAADANAQMAAACAaQgYAAAAAExDwAAAAABgGmaRqoQ6jlkiq83H3WUAAACUq6QpA9xdAsoBPRgAAAAATEPAAAAAAGAaAgYAAAAA0xAwAAAAAJiGgAEAAADANAQMAAAAAKYhYAAAAAAwDQEDAAAAgGkIGJLS0tIUFhamY8eOOdv27dun6OhoBQYGKiEhQYZhuBxz+PBh1apVq8hrvfrqqwoKClKNGjV0//33Ky0trbzLBwAAACqN6z5gpKWlqWfPni7hwuFwqFevXmrXrp127typlJQUJSYmOrcfPXpU3bt3V3p6ustrbd68WR9//LE2b96s3bt3q6CgQCNHjqygKwEAAADc77oPGI8++qj69+/v0rZmzRplZmbqjTfeUHh4uCZMmKB33nnHub1Xr14aOnRokdfasWOHunfvrubNm6tJkybq37+/Dh8+XOK5HQ6H7Ha7ywIAAABUZdd9wJg/f77i4+Nd2pKTkxUTEyNfX19JUmRkpFJSUpzbV69erX79+hV5rZYtW+qzzz7T0aNHdfbsWb3zzjvq3LlzieeeOHGi/P39nUtISIhJVwUAAAC4x3UfMMLCwoq02e12l3aLxSKr1eq8Jaq4YySpW7duCg8PV3h4uIKCgpSdna2//e1vJZ579OjRyszMdC4nT578nVcDAAAAuNd1HzCK4+npKZvN5tJWrVo15eTklHrcsmXLdOLECaWkpOjs2bNq2bKlHn/88RL3t9ls8vPzc1kAAACAqszT3QVURrVq1dK+fftc2rKysuTt7V3qcYsXL9bw4cN10003SZKmT5+ugIAAZWRkKCAgoLzKBQAAACoNejCKER0dra1btzrXU1NT5XA4ip2W9tcKCwt19uxZ5/qZM2ckSQUFBeVTKAAAAFDJ0INRjI4dO8put2vhwoUaPHiwJkyYoPvuu09Wq7XU4zp06KApU6aoYcOG8vHx0fTp03X77berdu3aFVQ5AAAA4F4EjGJ4enpqwYIFiouLU0JCgjw8PPT1119f8bgRI0bo5MmTevXVV5WWlqb27dtr4cKF5V8wAAAAUElYjN8+ohpOZ86cUVJSkmJiYiqkF8Jut8vf319tRsyR1eZT7ucDAABwp6QpA9xdAq7B5e+qmZmZpU5ORA9GKerVq6cePXq4uwwAAACgymCQNwAAAADTEDAAAAAAmIaAAQAAAMA0jMGohDaPj+Op3gAAAKiS6MEAAAAAYBoCBgAAAADTEDAAAAAAmIaAAQAAAMA0BAwAAAAApmEWqUqo45glstp83F0GAAC4TiVNGeDuElCF0YMBAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATEPAAAAAAGAaAgYAAAAA01S6gHHs2DFZLBZTX/Prr79WaGioqa95NRITE3X33XdX+HkBAAAAd6l0AaNRo0ZKT093dxlXZLFYdOzYMXeXAQAAAFQqlS5geHh4KCAgwN1lAAAAACiDCg8YiYmJuvXWW9W7d2/5+/srNjZWp0+fdm4v7hap06dPy9/fX9u2bZMkjR07VnfccYcMw5Akff/997rtttvk7++vBx54QJmZmb+7ziVLligsLEzVq1dX165dlZaWJkmKiIhw1hcWFiaLxaKPPvrIedyrr76qunXrqlmzZtq1a1ep53A4HLLb7S4LAAAAUJW5pQfj+++/V/v27bV7927ZbDYNGzas1P3r16+vsWPH6oUXXtDZs2c1Y8YMzZ49WxaLRRkZGerWrZu6deumPXv2yG636/nnn/9d9WVlZWngwIGaOHGi9u/fL09PT02dOtVZ++VbuJKTk5Wenq4HH3xQkrRy5UpNmzZNy5YtU2Jioj744INSzzNx4kT5+/s7l5CQkN9VNwAAAOBunu44aXBwsF544QVZLBa9/PLLio6O1sWLF+XpWXI58fHxevfdd9WzZ0898cQTatu2rSTpiy++kJeXl8aOHSuLxaJRo0ZpwIABv6s+T09PeXp6Ki8vT/Xr19fKlStVWFgoSapZs6ZzPz8/P5fbuT777DP1799fHTt2lCT96U9/0o4dO0o8z+jRozVy5Ejnut1uJ2QAAACgSnNLD0ZwcLDzNqOGDRuqoKBA586dK/UYT09PDRkyRN9//71Lj8cPP/ygn3/+WYGBgQoICNDDDz+sn3/+WRcuXChzfT4+Pvroo480b9481a1bV/fff79Onjx5xeNOnz6tRo0aOdfDw8NL3d9ms8nPz89lAQAAAKoytwSMEydOOMdPnDx5Up6enqpTp06px9jtdr3++uvq0qWL/vGPfzjbg4OD1a5dO+3evVu7d+9WcnKydu3aJS8vrzLXd/78eQUFBenbb7/VTz/9pDp16ugvf/mLyz4Wi8V5DZfVrVtXp06dcrlOAAAA4HriloBx6tQpTZw4UampqRo3bpx69+4tq9Va6jF///vfdfvtt+vTTz/Vtm3btHr1aklSjx49dOLECe3YsUM+Pj5atmyZYmNji3z5vxZnz57V3XffrbVr1+r8+fOSpIsXL7rsEx4erjVr1ujHH3/U5s2bJUn333+/Fi9erO+++07bt2/X/Pnzy1wDAAAAUBW5JWDExMRox44datWqlfLy8vTmm2+Wuv+OHTu0cOFCTZkyRTVq1NDkyZP15z//Wb/88osCAgK0cuVKTZ06VY0bN9bSpUu1cuXKUsdzXElERISmTp2q4cOHKzw8XIcOHdKUKVNc9nn77bc1depUhYWFae7cuZKkBx98UE899ZR69+6tgQMHqnfv3mWuAQAAAKiKLMbv+VV/GSQmJioxMVFff/11RZ62SrDb7fL391ebEXNktfm4uxwAAHCdSpry+ybMwR/T5e+qmZmZpY4drnQP2qsIJ06cUEBAQLHLo48+6u7yAAAAgCqrwqepHTRokAYNGlTRp3XRoEED7d69u9htvr6+FVsMAAAA8AfiludguJunp6dCQ0PdXQYAAADwh3Nd3iIFAAAAoHwQMAAAAACY5rq8Raqy2zw+jqd6AwAAoEqiBwMAAACAaQgYAAAAAExDwAAAAABgGgIGAAAAANMQMAAAAACYhlmkKqGOY5bIavNxdxkAAKASSpoywN0lAKWiBwMAAACAaQgYAAAAAExDwAAAAABgGgIGAAAAANMQMAAAAACYhoDxO2RkZGj79u1KT093dykAAABApUDAKKOlS5cqNDRUQ4YMUXBwsJYuXSpJWrFihRo3bixPT0+1bdtWBw4ccHOlAAAAQMUhYJRBZmamnn76aW3evFl79+7V7NmzlZCQoCNHjmjw4MGaNGmSfvzxRzVr1kxDhgxxd7kAAABAhSFglIHdbtf06dMVGRkpSYqKitK5c+d04MABTZo0SQ8//LCCgoI0fPhw7dq1y83VAgAAABWHJ3mXQUhIiB577DFJUn5+vqZNm6a+ffuqZ8+eLvsdOnRITZs2dUeJAAAAgFsQMH6H5ORk3XPPPfL29i4y1iIvL09Tp07VyJEjSzze4XDI4XA41+12e7nVCgAAAFQEbpH6HSIjI7Vu3To1bdq0yFiLsWPHqnr16qWOwZg4caL8/f2dS0hISHmXDAAAAJQri2EYhruLqOpSU1MVHh6u8+fPKyAgQBs2bFCfPn20bds2tWjRosTjiuvBCAkJUZsRc2S1+VRE6QAAoIpJmjLA3SXgOmW32+Xv76/MzEz5+fmVuB89GGWwadMmJSQkONe9vb1lsVjk4eGh1NRUxcXFafbs2aWGC0my2Wzy8/NzWQAAAICqjDEYZdCsWTPNmzdPTZs2Vbdu3TRmzBh16dJFXl5e6tmzp3r37q2+ffsqOztbklS9enVZLBY3Vw0AAACUP3owyqB+/fpatmyZZsyYoZYtWyonJ0eLFi3SunXrlJKSovnz56tmzZrO5fjx4+4uGQAAAKgQ9GCUUefOnbV//36Xtt69e4shLQAAALie0YMBAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATMMsUpXQ5vFxPHQPAAAAVRI9GAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIaAAQAAAMA0zCJVCXUcs0RWm4+7ywAA4A8vacoAd5cA/OHQgwEAAADANAQMAAAAAKYhYAAAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTEDBKsHDhQrVq1UoBAQGKi4tTWlpaqe2/9sILL6hXr14VXTIAAADgdgSMYqxfv17x8fGaNm2a9uzZI7vdrr59+5bY/mt79uzRW2+9pRkzZripegAAAMB9eJJ3MRYtWqRBgwapc+fOkqQpU6aoZcuWmjlzZrHt58+fV61atVRYWKihQ4fqueeeU+PGjd15CQAAAIBb0INRjLS0NDVq1Mi5brVaJUlZWVnFtl/+c86cOdq7d69CQ0O1cuVK5eXllXoeh8Mhu93usgAAAABVGQGjGFFRUVq9erUKCwslSYmJiYqOjtYdd9xRbLu/v7+ys7M1duxYNW7cWMePH9e0adN05513Kjc3t8TzTJw4Uf7+/s4lJCSkQq4PAAAAKC/cIlWMUaNGadOmTYqKipKPj4+2bdumRYsWqVevXsW2S9Ly5cv1yy+/aOPGjapTp44uXryo1q1b6/3339fQoUOLPc/o0aM1cuRI57rdbidkAAAAoEojYBQjICBA33zzjQ4fPqzXX39dGRkZ6t+/v6xWa7HtkvTDDz8oJiZGderUkSR5enoqMjJShw8fLvE8NptNNputQq4JAAAAqAgEjFI0aNBAy5cv17x585zjLEpqDw4OLnI71PHjx3X77bdXaM0AAACAOzEGoxSzZs1SRESE+vTpc8X2Hj16KCUlRXPmzNEPP/ygmTNnKjk5WQ888EDFFg0AAAC4ET0YJUhPT9fkyZO1du3aq2qvXbu2/vnPf2rUqFEaOXKk6tevr08++YQxFQAAALiuEDBKEBgYqHPnzl11uyTdcccd2rp1a3mXBgAAAFRa3CIFAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATMMsUpXQ5vFx8vPzc3cZAAAAwDWjBwMAAACAaQgYAAAAAExDwAAAAABgGgIGAAAAANMQMAAAAACYhlmkKqGOY5bIavNxdxkAAFQ6SVMGuLsEAFdADwYAAAAA0xAwAAAAAJiGgAEAAADANAQMAAAAAKYhYAAAAAAwDQEDAAAAgGkIGCbLyMjQ9u3blZ6e7u5SAAAAgApHwCjBwoUL1apVKwUEBCguLk5paWku21944QX16tXLpW3p0qUKDQ3VkCFDFBwcrKVLl1ZkyQAAAIDbETCKsX79esXHx2vatGnas2eP7Ha7+vbt69y+Z88evfXWW5oxY4azLTMzU08//bQ2b96svXv3avbs2UpISHBH+QAAAIDb8CTvYixatEiDBg1S586dJUlTpkxRy5Ytdf78eQUEBGjo0KF67rnn1LhxY+cxdrtd06dPV2RkpCQpKipK586dc0v9AAAAgLvQg1GMtLQ0NWrUyLlutVqdf86ZM0d79+5VaGioVq5cqby8PElSSEiIHnvsMUlSfn6+pk2b5tLrURyHwyG73e6yAAAAAFUZAaMYUVFRWr16tQoLCyVJiYmJio6OltVq1dixY9W4cWMdP35c06ZN05133qnc3FznscnJyapXr57Wrl2rmTNnlnqeiRMnyt/f37mEhISU63UBAAAA5c1iGIbh7iIqm4yMDPXq1UtZWVny8fHRtm3btGjRIhmGoWHDhunEiROqU6eOLl68qNatW+u5557T0KFDJUmGYejf//63nnvuOdWtW1fLli0r8TwOh0MOh8O5brfbFRISojYj5shq8yn36wQAoKpJmjLA3SUA1y273S5/f39lZmbKz8+vxP3owShGQECAvvnmGy1btkxt2rRRRESE+vfvrx9++EExMTGqU6eOJMnT01ORkZE6fPiw81iLxaJ27drpvffe0/Lly5WRkVHieWw2m/z8/FwWAAAAoCojYJSiQYMGWr58uSZOnCir1arg4GCX26Ek6fjx42rYsKE2bdrkMmuUt7e3LBaLPDz4EQMAAOD6wbffUsyaNUsRERHq06ePJKlHjx5KSUnRnDlz9MMPP2jmzJlKTk7WAw88oGbNmmnevHmaN2+eTp48qRdffFFdunShVwIAAADXFQJGCdLT0zV58mRNnTrV2Va7dm3985//1HvvvadmzZppxowZ+uSTTxQSEqL69etr2bJlmjFjhlq2bKmcnBwtWrTIjVcAAAAAVDyeg1GCwMDAYp9jcccdd2jr1q3FHtO5c2ft37+/vEsDAAAAKi16MAAAAACYhoABAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpmEWqEto8Po7nZwAAAKBKogcDAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIZZpCqhjmOWyGrzcXcZAIA/uKQpA9xdAoA/IHowAAAAAJiGgAEAAADANAQMAAAAAKYhYAAAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBoCRini4+NlsVicS5MmTSRJ+/btU3R0tAIDA5WQkCDDMCRJhmFo8uTJatq0qerUqaNnnnlGv/zyizsvAQAAAKhQBIxS7Ny5U1988YXS09OVnp6uXbt2yeFwqFevXmrXrp127typlJQUJSYmSpLeeecdzZgxQ4sXL9aWLVu0Y8cODRs2zL0XAQAAAFQgAkYJLl68qP3796tjx44KCAhQQECAatasqTVr1igzM1NvvPGGwsPDNWHCBL3zzjuSpEWLFmnkyJG69dZb1bx5c40bN04rVqxw85UAAAAAFYeAUYK9e/eqsLBQbdu2lY+Pj2JjY3XixAklJycrJiZGvr6+kqTIyEilpKRIktLS0tSoUSPna1itVlmt1hLP4XA4ZLfbXRYAAACgKiNglCAlJUXNmzfX+++/rz179sjT01NDhw6V3W5XWFiYcz+LxSKr1ar09HRFRUW59FgkJiaqc+fOJZ5j4sSJ8vf3dy4hISHlek0AAABAebMYl0coo1QnTpxQWFiYc+D3G2+84dwWEhKibdu2qaCgQN26dVNAQICysrK0d+9ebd68WR06dCj2NR0OhxwOh3PdbrcrJCREbUbMkdXmU+7XBAC4viVNGeDuEgBUIXa7Xf7+/srMzJSfn1+J+3lWYE1VWt26dVVYWKh69epp3759LtuysrLk7e2tG264Qfv27dPBgwf117/+VfXq1SsxXEiSzWaTzWYr79IBAACACsMtUiVISEjQhx9+6FzfunWrPDw81Lp1a23dutXZnpqaKofDoVq1akm6dMuUn5+f1q9fr9dee63C6wYAAADciR6MErRp00ZjxoxRUFCQCgoKNGLECA0YMEBdunSR3W7XwoULNXjwYE2YMEH33Xefy2Du8ePH66GHHtLNN9/sxisAAAAAKh4BowSPP/649u/frwcffFBWq1WPP/64JkyYIE9PTy1YsEBxcXFKSEiQh4eHvv76a+dxhw8f1ocffqj9+/e7r3gAAADATRjkXUZnzpxRUlKSYmJiVLt2bVNe8/LAGQZ5AwAqAoO8AVwLBnmXs3r16qlHjx7uLgMAAACoVBjkDQAAAMA0ZQoY+fn5mjBhgm677TY1bNhQ+/fv16233qojR46YXR8AAACAKqRMAePpp5/WJ598oieffFJZWVny9fXV7bffrqeeesrs+gAAAABUIWUKGMuWLdOnn36qp556SlarVVarVS+88IK2b99udn0AAAAAqpAyDfIOCQnR5s2bFR4eLunSw+X279+vsLAwU4u7Xm0eH1fqyHwAAACgsipTwJg8ebL69OmjefPmKScnRyNHjtQ333yjRYsWmV0fAAAAgCqkzM/BOHz4sD755BP98MMPCg4O1qOPPqrGjRubXd915WrnFgYAAAAq2tV+V+VBe5UIAQMAAACV1dV+Vy3TIO+1a9cqPT29zMUBAAAA+GMqU8AYNmyYdu7caXYtAAAAAKq4Mg3yjo+P1/Tp09WpUyd5epbpJVCKjmOWyGrzcXcZAIBKIGnKAHeXAADXpEzpoHbt2kpPT1e7du00bNgwVa9e3bltwAA+CAEAAIDrVZkCRmJiomw2m2w2mz755BNnu8ViIWAAAAAA17EyBYyNGzeaXQcAAACAP4AyDfIGAAAAgOKUqQfDw8NDFoul2G0FBQW/qyAAAAAAVVeZAkZqaqrz7zk5Odq5c6emTJmiv//976YVBgAAAKDqKVPAuPHGG13Wb7rpJsXGxqpXr1565JFHTCkMAAAAQNVj2hgMHx8fnTlzxqyXq9L27dun6OhoBQYGKiEhQYZhuLskAAAAoEKUqQejU6dOLmMwCgsLlZKSoi5duphWWFXlcDjUq1cvde3aVR999JHi4+OVmJiowYMHu7s0AAAAoNyVKWAMGjTIZd1isahhw4a65557zKipSluzZo0yMzP1xhtvyNfXVxMmTNAzzzxTbMBwOBxyOBzOdbvdXpGlAgAAAKYrU8AYOHCg2XX8YSQnJysmJka+vr6SpMjISKWkpBS778SJEzVu3LiKLA8AAAAoV2UagzF58mTl5+e7tG3YsEF33XWXKUVVZXa7XWFhYc51i8Uiq9Wq9PT0IvuOHj1amZmZzuXkyZMVWSoAAABgujIFjNGjRys3N9elrUWLFtq2bZspRVVlnp6estlsLm3VqlVTTk5OkX1tNpv8/PxcFgAAAKAqu6ZbpDZv3ixJMgxDW7ZsUfXq1Z3r69atU0REhPkVVjG1atXSvn37XNqysrLk7e3tpooAAACAinNNAePy2AuLxaJhw4bJw+NSB4iHh4eaNGmiRYsWmV9hFRMdHa358+c711NTU+VwOFSrVi03VgUAAABUjGsKGJef4O3h4aG9e/dyS08xOnbsKLvdroULF2rw4MGaMGGC7rvvPlmtVneXBgAAAJS7Ms0i1bVrV3l5eZldyx+Cp6enFixYoLi4OCUkJMjDw0Nff/21u8sCAAAAKkSZAsaaNWuKbc/Ly2OsgaT7779fR44cUVJSkmJiYlS7dm13lwQAAABUiDIFjDNnzmj8+PE6dOiQCgoKJF0a6H3gwAGdOXPG1AKrqnr16qlHjx7uLgMAAACoUGWapvaJJ57Q+fPn5evrKx8fHz300EM6dOiQnn76abPrAwAAAFCFlClgbNu2TdOmTVNCQoKysrI0fPhwvfPOO1q7dq3Z9QEAAACoQsoUMBo2bKh169YpOjpaKSkpys3NVatWrbR3716z6wMAAABQhZRpDMZrr72muLg4xcbGqlevXrrllltkGIbuvPNOs+u7Lm0eH8cUwAAAAKiSLIZhGGU5MCMjQzVq1JBhGPrwww+VlZWlgQMHqmbNmmbXeN2w2+3y9/dXZmYmAQMAAACVytV+Vy1TD4YkBQQESLo0Ne0TTzwhSc4newMAAAC4PpUpEWRlZWno0KEKCgqSr6+v9u3bp+DgYCUlJZldHwAAAIAqpEwBY/DgwTp27Jjee+89Va9eXf7+/nruuef0zDPPmF0fAAAAgCqkTGMwAgICtHfvXoWEhCgwMFDJycmqVq2awsPDlZWVVR51XhcYgwEAAIDKqlzHYERERCgxMVEvvfSSLBaLLBaLNm/erJYtW5a5YPxPxzFLZLX5uLsMAICbJE0Z4O4SAKDMyhQwZs2ape7du2vOnDnKysrSww8/rBMnTmjlypVm1wcAAACgCrnqgJGUlKRGjRrphhtuUHR0tA4fPqwvvvhCP/zwg4KDg9WjRw/5+/uXZ60AAAAAKrmrDhg9evTQpk2bdMMNN6hFixb6/vvv1b9///KsDQAAAEAVc9WzSGVlZSkwMFCSdPDgQRUWFpZbUQAAAACqpqvuwejUqZN69Oih1q1bS5KGDx8ub2/vIvu9++675lUHAAAAoEq56oDx0UcfKTExUefOnZPFYlFISIiqVatWnrUBAAAAqGLK9BwMDw8PZWRk8KwGk12eW7jNiDlMUwsA1zGmqQVQGV3tczDK9CTvJUuWqHr16mUu7o8uLS1NYWFhOnbsmLtLAQAAACpUmZ6D8cgjj5hdxx9GWlqaevbsSbgAAADAdalMPRgo2aOPPsr0vQAAALhulakHAyWbP3++wsLC9Oyzz15xX4fDIYfD4Vy32+3lWRoAAABQ7ujBMFlYWNhV7ztx4kT5+/s7l5CQkHKsDAAAACh/BAw3Gj16tDIzM53LyZMn3V0SAAAA8Ltwi5Qb2Ww22Ww2d5cBAAAAmIYeDAAAAACmIWAAAAAAMA0BAwAAAIBpGINRTgzDcHcJAAAAQIWjBwMAAACAaQgYAAAAAExDwAAAAABgGgIGAAAAANMQMAAAAACYhlmkKqHN4+Pk5+fn7jIAAACAa0YPBgAAAADTEDAAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANs0hVQh3HLJHV5uPuMgAA/7+kKQPcXQIAVBn0YAAAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIaAcQUZGRnavn270tPT3V0KAAAAUOkRMEqxdOlShYaGasiQIQoODtbSpUslSStWrFDjxo3l6emptm3b6sCBA85j9u3bp+joaAUGBiohIUGGYbirfAAAAKDCETBKkJmZqaefflqbN2/W3r17NXv2bCUkJOjIkSMaPHiwJk2apB9//FHNmjXTkCFDJEkOh0O9evVSu3bttHPnTqWkpCgxMdG9FwIAAABUIAJGCex2u6ZPn67IyEhJUlRUlM6dO6cDBw5o0qRJevjhhxUUFKThw4dr165dkqQ1a9YoMzNTb7zxhsLDwzVhwgS988477rwMAAAAoEJ5uruAyiokJESPPfaYJCk/P1/Tpk1T37591bNnT5f9Dh06pKZNm0qSkpOTFRMTI19fX0lSZGSkUlJSSjyHw+GQw+FwrtvtdrMvAwAAAKhQBIwrSE5O1j333CNvb2+XsRaSlJeXp6lTp2rkyJGSLgWEsLAw53aLxSKr1ar09HQFBgYWee2JEydq3Lhx5XsBAAAAQAXiFqkriIyM1Lp169S0aVPnWIvLxo4dq+rVqzvbPT09ZbPZXPapVq2acnJyin3t0aNHKzMz07mcPHmyfC4CAAAAqCD0YFyBxWJRu3bt9N577yk8PFwZGRkKCAjQhg0bNHv2bG3btk1eXl6SpFq1amnfvn0ux2dlZcnb27vY17bZbEUCCQAAAFCV0YNRgk2bNikhIcG57u3tLYvFIg8PD6WmpiouLk6zZ89WixYtnPtER0dr69atzvXU1FQ5HA7VqlWrQmsHAAAA3IWAUYJmzZpp3rx5mjdvnk6ePKkXX3xRXbp0kZeXl3r27KnevXurb9++ys7OVnZ2tgzDUMeOHWW327Vw4UJJ0oQJE3TffffJarW6+WoAAACAikHAKEH9+vW1bNkyzZgxQy1btlROTo4WLVqkdevWKSUlRfPnz1fNmjWdy/Hjx+Xp6akFCxboz3/+s+rUqaMVK1botddec/elAAAAABXGYvCoadOdOXNGSUlJiomJUe3ata/6OLvdLn9/f7UZMUdWm085VggAuBZJUwa4uwQAcLvL31UzMzPl5+dX4n4M8i4H9erVU48ePdxdBgAAAFDhuEUKAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTMMi7Eto8Pq7UkfkAAABAZUUPBgAAAADTEDAAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANs0hVQh3HLJHV5uPuMgBAkpQ0ZYC7SwAAVCH0YAAAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIaAAQAAAMA0BIxSpKWlKSwsTMeOHXO2LVy4UK1atVJAQIDi4uKUlpZ2VdsAAACA6wEBowRpaWnq2bOnS7hYv3694uPjNW3aNO3Zs0d2u119+/a94jYAAADgesGTvEvw6KOPqn///tq+fbuzbdGiRRo0aJA6d+4sSZoyZYpatmyp8+fPl7qtVq1abrkGAAAAoKLRg1GC+fPnKz4+3qUtLS1NjRo1cq5brVbnn6VtK4nD4ZDdbndZAAAAgKqMgFGCsLCwIm1RUVFavXq1CgsLJUmJiYmKjo6Wv79/qdtKMnHiRPn7+zuXkJCQ8rkYAAAAoIJwi9Q1GDVqlDZt2qSoqCj5+Pho27ZtWrRo0RW3lWT06NEaOXKkc91utxMyAAAAUKURMK5BQECAvvnmGx0+fFivv/66MjIy1L9//ytuK4nNZpPNZquI0gEAAIAKQcAogwYNGmj58uWaN29ekTEWpW0DAAAA/ugYg1EGs2bNUkREhPr06XNN2wAAAIA/OnowrlF6eromT56stWvXXtM2AAAA4HpAwLgCwzBc1gMDA3Xu3Lli9y1tGwAAAHA94BYpAAAAAKYhYAAAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBpmkaqENo+Pk5+fn7vLAAAAAK4ZPRgAAAAATEPAAAAAAGAaAgYAAAAA0xAwAAAAAJiGgAEAAADANMwiVQl1HLNEVpuPu8sAcB1LmjLA3SUAAKooejAAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANAQMAAACAaQgYAAAAAExDwCjBwoUL1apVKwUEBCguLk5paWmSpAULFigkJES+vr66++67dfTo0SLHvvDCC+rVq1dFlwwAAAC4HQGjGOvXr1d8fLymTZumPXv2yG63q2/fvjpy5IheeeUVrVixQgcPHlR4eLgGDRrkcuyePXv01ltvacaMGe4pHgAAAHAjHrRXjEWLFmnQoEHq3LmzJGnKlClq2bKlkpKSFBMTo6ioKEnSk08+qYceesh5XGFhoYYOHarnnntOjRs3dkvtAAAAgDvRg1GMtLQ0NWrUyLlutVolSc2bN9eGDRu0e/duZWZm6q233nKGEEmaM2eO9u7dq9DQUK1cuVJ5eXmlnsfhcMhut7ssAAAAQFVGwChGVFSUVq9ercLCQklSYmKioqOj1aZNG/Xr108333yzAgICtHXrVr3++uuSpOzsbI0dO1aNGzfW8ePHNW3aNN15553Kzc0t8TwTJ06Uv7+/cwkJCamQ6wMAAADKCwGjGKNGjVJhYaGioqLUvn17TZo0SSNGjNCOHTu0atUqbdu2TRkZGYqLi1P37t1lGIaWL1+uX375RRs3btS4ceP01VdfKSsrS++//36J5xk9erQyMzOdy8mTJyvwKgEAAADzETCKERAQoG+++UbLli1TmzZtFBERof79+2vJkiV69NFHddttt8nf31/jx4/XkSNHlJycrB9++EExMTGqU6eOJMnT01ORkZE6fPhwieex2Wzy8/NzWQAAAICqjIBRigYNGmj58uWaOHGirFarCgsLdfbsWef2rKws5eTkqKCgQMHBwUVuhzp+/LgaNmxY0WUDAAAAbsMsUqWYNWuWIiIi1KdPH0lShw4dNHDgQEVFRSkoKEgLFixQvXr1FBkZqdDQUI0YMUJz5sxRz549tXz5ciUnJ2vp0qXuvQgAAACgAhEwSpCenq7Jkydr7dq1zrYHH3xQBw4c0PTp03X69Gm1atVKn332mby8vFS7dm3985//1KhRozRy5EjVr19fn3zyCQO3AQAAcF2xGIZhuLsIXGK32+Xv7682I+bIavNxdzkArmNJUwa4uwQAQCVz+btqZmZmqWOHGYMBAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATMM0tZXQ5vFxPNUbAAAAVRI9GAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIaAAQAAAMA0zCJVCXUcs0RWm4+7ywDwB5A0ZYC7SwAAXGfowQAAAABgGgIGAAAAANMQMAAAAACYhoABAAAAwDQEDAAAAACmIWAAAAAAMA0BAwAAAIBpCBhXKTY2VomJiZKkV199VUFBQapRo4buv/9+paWlOfebN2+e6tevLy8vL9111106ffq0myoGAAAAKh4B4yosXrxYX375pSRp8+bN+vjjj7V582bt3r1bBQUFGjlypCTp22+/1UsvvaT3339fqampMgxDo0aNcmfpAAAAQIUiYFzB+fPn9fzzz6t58+aSpB07dqh79+5q3ry5mjRpov79++vw4cOSpP/+97+aO3eu7rvvPgUHB2vw4MHatWuXO8sHAAAAKpSnuwuo7J5//nn17dtXubm5kqSWLVtq7ty5GjZsmGrUqKF33nlHnTt3liQNHjzY5dhDhw6padOmJb62w+GQw+Fwrtvt9nK4AgAAAKDi0INRio0bN+pf//qXJk+e7Gzr1q2bwsPDFR4erqCgIGVnZ+tvf/tbkWPPnz/vDCIlmThxovz9/Z1LSEhIuVwHAAAAUFEIGCW4cOGCnnrqKb399tuqWbOms33ZsmU6ceKEUlJSdPbsWbVs2VKPP/54keOfeeYZ3X777erWrVuJ5xg9erQyMzOdy8mTJ8vlWgAAAICKwi1SJXj11VcVHR2tHj16uLQvXrxYw4cP10033SRJmj59ugICApSRkaGAgABJ0nvvvaeNGzcqOTm51HPYbDbZbLZyqR8AAABwBwJGCT788EP9/PPPztCQk5OjTz75RHfddZfOnj3r3O/MmTOSpIKCAknSzp07NWLECK1cuVJBQUEVXjcAAADgTgSMEnzzzTe6ePGic33UqFGKiYmRJE2ZMkUNGzaUj4+Ppk+frttvv121a9fW2bNn1atXL/31r3/VLbfcouzsbElSjRo13HINAAAAQEUjYJQgODjYZb1GjRqqU6eO4uLidPLkSb366qtKS0tT+/bttXDhQknSkiVLdObMGb300kt66aWXnMcahlGhtQMAAADuYjH49ltp2O12+fv7q82IObLafNxdDoA/gKQpA9xdAgDgD+Lyd9XMzEz5+fmVuB+zSAEAAAAwDQEDAAAAgGkIGAAAAABMQ8AAAAAAYBoCBgAAAADTME1tJbR5fFypI/MBAACAyooeDAAAAACmIWAAAAAAMA0BAwAAAIBpCBgAAAAATEPAAAAAAGAaZpGqhDqOWSKrzcfdZQCoopKmDHB3CQCA6xg9GAAAAABMQ8AAAAAAYBoCBgAAAADTEDAAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANAaMUCxYsUEhIiHx9fXX33Xfr6NGjkqSFCxeqVatWCggIUFxcnNLS0oo9PjY2VomJiRVYMQAAAOBeBIwSHDlyRK+88opWrFihgwcPKjw8XIMGDdL69esVHx+vadOmac+ePbLb7erbt2+R4xcvXqwvv/zSDZUDAAAA7sOTvEuwa9cuxcTEKCoqSpL05JNP6qGHHtKiRYs0aNAgde7cWZI0ZcoUtWzZUufPn1etWrUkSefPn9fzzz+v5s2bu61+AAAAwB0IGCVo0aKFNmzYoN27dyssLExvvfWWOnfurJ9//lmtW7d27me1Wl3+lKTnn39effv2VW5ubqnncDgccjgcznW73W7yVQAAAAAVi1ukStCiRQv169dPN998swICArR161a9/vrrioqK0urVq1VYWChJSkxMVHR0tPz9/SVJGzdu1L/+9S9Nnjz5iueYOHGi/P39nUtISEi5XhMAAABQ3ggYJdixY4dWrVqlbdu2KSMjQ3Fxcerevbuef/55FRYWKioqSu3bt9ekSZM0YsQISdKFCxf01FNP6e2331bNmjWveI7Ro0crMzPTuZw8ebK8LwsAAAAoVwSMEixZskSPPvqobrvtNvn7+2v8+PE6cuSIjh8/rm+++UbLli1TmzZtFBERof79+0uSXn31VUVHR6tHjx5XdQ6bzSY/Pz+XBQAAAKjKGINRgsLCQpfpZ7OyspSTk6OCggJJUoMGDbR8+XLNmzfPOf7iww8/1M8//6yAgABJUk5Ojj755BPt2LFDb731VoVfAwAAAFDRCBgl6NChgwYOHKioqCgFBQVpwYIFqlevniIjIyVJs2bNUkREhPr06eM85ptvvtHFixed66NGjVJMTIwGDRpUwdUDAAAA7kHAKMGDDz6oAwcOaPr06Tp9+rRatWqlzz77TF5eXkpPT9fkyZO1du1al2OCg4Nd1mvUqKE6deqoTp06FVk6AAAA4DYWwzAMdxeBS+x2u/z9/dVmxBxZbT7uLgdAFZU0ZYC7SwAA/AFd/q6amZlZ6thhBnkDAAAAMA0BAwAAAIBpCBgAAAAATEPAAAAAAGAaAgYAAAAA0zBNbSW0eXwcT/UGAABAlUQPBgAAAADTEDAAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANs0hVQh3HLJHV5uPuMgBUAUlTBri7BAAAXNCDAQAAAMA0BAwAAAAApiFgAAAAADANAQMAAACAaQgYAAAAAExDwAAAAABgGgIGAAAAANMQMEqxYMEChYSEyNfXV3fffbeOHj1aarskLVy4UK1atVJAQIDi4uKUlpbmrvIBAACACkfAKMGRI0f0yiuvaMWKFTp48KDCw8M1aNCgEtslaf369YqPj9e0adO0Z88e2e129e3b170XAgAAAFQgAkYJdu3apZiYGEVFRalRo0Z68skndfjw4RLbJWnRokUaNGiQOnfurEaNGmnKlCn69ttvdf78eTdfDQAAAFAxCBglaNGihTZs2KDdu3crMzNTb731ljp37lxiuySlpaWpUaNGztewWq0uf/6Ww+GQ3W53WQAAAICqzNPdBVRWLVq0UL9+/XTzzTdLksLCwrR9+3bdcMMNxbZLUlRUlFavXq3nn39eHh4eSkxMVHR0tPz9/Ys9x8SJEzVu3LiKuSAAAACgAtCDUYIdO3Zo1apV2rZtmzIyMhQXF6fu3btr+/btxbYbhqFRo0apsLBQUVFRat++vSZNmqQRI0aUeI7Ro0crMzPTuZw8ebICrxAAAAAwn8UwDMPdRVRGzz33nDw8PDR16lRJkmEYql27ttq0aaOoqKgi7Rs2bFDbtm0lSYcPH9brr7+uTZs2ad++fSXeIvVbdrtd/v7+ajNijqw2n3K5LgB/LElTBri7BADAdeLyd9XMzEz5+fmVuB+3SJWgsLDQZYrZrKws5eTkqEaNGjp79myR9oKCAmdbgwYNtHz5cs2bN++qwwUAAADwR0DAKEGHDh00cOBARUVFKSgoSAsWLFC9evU0YMAADRo0qEh7ZGSk89hZs2YpIiJCffr0cd8FAAAAAG5AwCjBgw8+qAMHDmj69Ok6ffq0WrVqpc8++0xt27bVwYMHi7R7eXlJktLT0zV58mStXbvWzVcAAAAAVDzGYFQijMEAcK0YgwEAqChXOwaDWaQAAAAAmIaAAQAAAMA0BAwAAAAApiFgAAAAADANAQMAAACAaZimthLaPD6u1JH5AAAAQGVFDwYAAAAA09CDUYlcfiSJ3W53cyUAAACAq8vfUa/0GD0CRiVy7tw5SVJISIibKwEAAACKl5WVJX9//xK3EzAqkVq1akmSTpw4Uep/NOAyu92ukJAQnTx5knE7uGq8b3CteM/gWvGe+WMyDENZWVlq0KBBqfsRMCoRD49LQ2L8/f35nxHXxM/Pj/cMrhnvG1wr3jO4Vrxn/niu5pfgDPIGAAAAYBoCBgAAAADTEDAqEZvNprFjx8pms7m7FFQRvGdQFrxvcK14z+Ba8Z65vlmMK80zBQAAAABXiR4MAAAAAKYhYAAAAAAwDQEDAAAAgGkIGAAAAABMQ8CoJPbt26fo6GgFBgYqISFBjL1HceLj42WxWJxLkyZNJPH+gau0tDSFhYXp2LFjzrbS3iObNm3STTfdpDp16uiNN95wQ8WoDIp735T0mSPxuXO9W7FihRo3bixPT0+1bdtWBw4ckMRnDS4hYFQCDodDvXr1Urt27bRz506lpKQoMTHR3WWhEtq5c6e++OILpaenKz09Xbt27eL9AxdpaWnq2bOny5fE0t4jP//8s+6//37FxcVp69atWrx4sTZu3Oie4uE2xb1vpOI/cyT+3breHTlyRIMHD9akSZP0448/qlmzZhoyZAifNfgfA2732WefGYGBgcYvv/xiGIZh7N6927jjjjvcXBUqm/z8fMPPz8/Iyspyaef9g1+79957jRkzZhiSjNTUVMMwSn+PTJs2zYiIiDAKCwsNwzCMzz//3HjsscfcUjvcp7j3TUmfOYbB5871btWqVcbcuXOd6xs2bDB8fHz4rIETPRiVQHJysmJiYuTr6ytJioyMVEpKipurQmWzd+9eFRYWqm3btvLx8VFsbKxOnDjB+wcu5s+fr/j4eJe20t4jycnJ6tSpkywWiyTp1ltvVVJSUsUWDbcr7n1T0meOxL9b17uePXtq6NChzvVDhw6padOmfNbAiYBRCdjtdoWFhTnXLRaLrFar0tPT3VgVKpuUlBQ1b95c77//vvbs2SNPT08NHTqU9w9c/Pq9cFlp75HfbvPz89OpU6cqpFZUHsW9b0r6zJH4dwv/k5eXp6lTp2rYsGF81sCJgFEJeHp6ymazubRVq1ZNOTk5bqoIldFjjz2mnTt3qn379mratKneeustffXVVyosLOT9g1KV9hnz2228d3BZSZ85drudf7fgNHbsWFWvXl1DhgzhswZOBIxKoFatWvr5559d2rKysuTt7e2milAV1K1bV4WFhapXrx7vH5SqtM+Y327jvYOSXP7MOX36NP9uQZK0YcMGzZ49Wx9++KG8vLz4rIETAaMSiI6O1tatW53rqampcjgcqlWrlhurQmWTkJCgDz/80Lm+detWeXh4qHXr1rx/UKrSPmN+u23Xrl1q2LChO8pEJVPSZ05ISAj/bkGpqamKi4vT7Nmz1aJFC0l81uB/CBiVQMeOHWW327Vw4UJJ0oQJE3TffffJarW6uTJUJm3atNGYMWP0r3/9S+vWrdOwYcM0YMAAdenShfcPSlXaZ8z999+vLVu2aP369crPz9fkyZPVtWtXN1eMyqCkzxxfX1/+3brO5ebmqmfPnurdu7f69u2r7OxsZWdnq0OHDnzW4BJ3T2OFS1asWGH4+voatWvXNm644QZj//797i4JldDf/vY3w9/f36hVq5YRHx9vZGdnG4bB+wdF6VfTjRpG6e+Rt99+2/Dy8jICAwONsLAw48yZM26oGJXBb983JX3mGAafO9ezzz//3JBUZElNTeWzBoZhGIbFMHj0ZmVx5swZJSUlKSYmRrVr13Z3OahieP/gSkp7j6SmpurgwYPq0KGDatSo4aYKUdXwuYPi8FkDAgYAAAAA0zAGAwAAAIBpCBgAAAAATEPAAAAAAGAaAgYAAAAA0xAwAAAV6uLFizp27JjWrl2rN99809leUFCg3NxcXc3cIxcvXlRubq5L27lz55SdnV3mugoKCpx/P3v2rNLS0sr8WgBwPfN0dwEAgD8mwzA0aNAg/fDDD8rNzVVGRobS09OVn5+vRo0aKSgoSEFBQUpJSVGLFi20d+9ePfTQQ/Lx8ZGHh4ccDoeOHDmiFi1a6MKFCzp+/LiaN28uScrPz1fjxo21atUq5/mefPJJdenSRc8884xLHePHj9eRI0ecD/8qTn5+viIjIzV37lx17NhRL774onx9fTVz5swi+2ZnZ2vGjBl68cUXde+99yo+Pl42m035+fnq2rWrbr75Zn311Vc8pRjAdYuAAQAoFxaLRWPGjFFhYaFq166twMBA/elPf1KrVq00atSoIvu3bdtW//3vf53rBw8eVGxsrHbv3q19+/bp8ccf1+7du0s8n81mU7Vq1ZzrERERWrdunXx8fOTj41NqrR9++KFycnJ02223SZJeeukltWrVSk8++aTatm3rsq+vr6+2b9+uWbNmydPTU97e3po8ebLGjRunPXv2KCMjQw0aNLiKnxAA/DERMAAApvvpp58UERGhmjVrysPjf3fjpqWlaeXKlS63RhUWFsput+vHH3+U3W5X+/btJV3qVfjpp58UGhqq/Px8/fzzzwoNDZUkNW/eXF9++aWkS7c2FRQUyMPDQ0ePHtX69esVERGhU6dOKTg4WFar1VlDfn6+LBaLPD3/98+f3W7XSy+9pPHjx8tms0mSbrzxRj333HN65JFHtHnzZgUFBTn3/89//qNu3brJ29tb6enp2rFjh+rVq6esrCx99913uvfee2WxWCRJeXl58vDwcDkfAPzR8aA9AECFGTRoUIk9GNKlYBIeHq7s7GxnD8axY8dcejC+/vprjRo1Sjt37pQk7dq1S8OGDVNYWJhq1aql7777Tq+88ooefvhh1ahRQxcuXFBBQYGqV6+u/Px8LViwQA899JDznH/605908OBBffPNNy5h6OLFi+ratat+/PFHrVmzRmFhYZKkNWvW6JNPPtGOHTuUkpKi7t27q2HDhqpZs6Z27Nih//73v6pWrZrOnTsnb29vffTRR+rcuXM5/lQBoHJhkDcAoNxs3bpVNWrUcC6LFy/W3//+d5e2vXv3/q5zHD58WE2aNJEk3XrrrfLw8ND777+vf/zjH0pLS9P48eP1pz/9SWlpacrMzHQJF7NmzdKnn36quXPnqqCgQBcvXnQukrR8+XKFhoaqdevWmjZtmiSpW7du6tGjhwoKCnTLLbcoJCRE1atX17PPPqstW7bo22+/1bFjx9S9e3fNnTuXcAHgukOfLQCg3FitVgUHB+vrr7+WJD377LOKiIjQ8OHDJUnR0dEutw8ZhqHQ0FC1atVKkuTp6anGjRvL19dXkpztTZs2dR6za9cu3XTTTdq3b58kafPmzWrUqJEmTJhQam3btm1TQkKC3nzzTbVu3brE/SZNmqR27do5Z6h69dVXtWbNGv3rX//SM888o+7du2vp0qV69tlnZbFYdOTIETVp0kSnTp1iLAaA6xIBAwBQbiwWi44ePaqYmBhJl8ZgfPXVV3rvvfckST/++KNzvILD4dANN9zgDArSpZmhcnJy9NFHH7m8bmFhoTIyMuTr66vIyEiFh4c7j7PZbPr444/VsGHDIlPe5ubmymazycPDQzExMTpw4IBCQ0PVv39/HTx4UJ06ddK5c+ec+3fs2FE33nijHn30UWfbn//8ZyUkJGjZsmXy8vLS/fffr/vvv1/dunVTfHy8tmzZoq5duyo1NVXNmjUz8acJAFUDAQMAUG7y8/NVq1YtDRs2TJK0dOlS1atXTx06dJAkjRs3znk7Unx8vL744gtnj0ZaWpoMw9ANN9yg0NBQZWVlKScnR0FBQTIMQw6HQ8uXL3f58i9JXl5euueee1SjRg35+PgoLy9PBQUFWrJkiXJzc7Vr1y7ndLeXx1X4+vpq3759at26tUuPyqlTpxQSEuLy+i+99JI+//xz/fTTT6pbt65CQ0N16tQpffDBB4qMjNQjjzyiwYMHy9fXV3Xq1CmHnyoAVG4EDABAuWnWrJnLjFF+fn6qU6eOc8zE+++/r+DgYEnS3Llznftt2bJFDz74oNauXau2bdsqLy9Pd9xxh5599lk9/vjjVzyv1Wp1Pohv+vTpOnz4sEsdxVm8eLFiY2Od64Zh6PTp0876LnvzzTcVHx+v3r17a+/evTp37pzatm2rHj16qHr16vLx8dGQIUPUvXv3K9YJAH9EBAwAQLnp2bOncnNznc+hOHLkiI4ePapDhw5Jki5cuKB//OMfSklJcR5jGIZWrVqlBg0aqEePHurYsaOysrLUrFmzqwoXZTF//nx9//33ev/9951tP//8sy5evFjsOIqdO3eqWrVq6ty5s/Lz8/Xggw+qevXqkqRhw4Zp8ODBeuONN8qlVgCo7JimFgBQYa40Te2v2e12LV26VH/961/Vrl077d27V+Hh4erevbseeOABRUREuOzfr18/xcbGasiQIbpw4YK8vb3l4eHh0oNRWFio/Px85/MucnNz9dprr2natGlavXq1OnTooPz8fO3Zs0crVqzQxx9/7AxDv3XhwgU9+eST+vHHH5Wbm6sHHnhAPXr0UL9+/VS3bl15eHjoiy++UI0aNX7/Dw4AqhB6MAAA5aJRo0by9vZ2ebp2WlqaNmzYoMTERGdbfn6+srKydPjwYT311FM6e/asUlNTlZ6ert69e2vDhg1q06aNCgoKtHnzZr333nuKiorSsGHDXHoJ8vPzdeHCBf3yyy9q1KiRczD3ZZ9//rkKCgp04403atu2bSosLNT999+vn3/+WV9//bVuvvlmSZfGcMTHxys/P19vv/12kes6cOCAFi9erKVLlyouLk7vvfeesrOzNXbsWMXGxuqdd97Rfffdpz59+qht27b6/PPPnbNfAcD1gB4MAEClsWbNGuXk5Oimm25SRESES0D4tYyMDHl5eTlvSyqr8+fPy8/P75qetH3y5El99NFHGjx4sHMQt8Ph0MyZM13aCgsL9dFHH+nRRx8t8ToA4I+IgAEAAADANPxKBQAAAIBpCBgAAAAATEPAAAAAAGAaAgYAAAAA0xAwAAAAAJiGgAEAAADANAQMAAAAAKYhYAAAAAAwzf8HHyIOWintheAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. 混淆矩阵和特征重要性分析\n",
    "print(\"\\n=== 步骤6：混淆矩阵和特征重要性分析 ===\")\n",
    "\n",
    "# 6.1 混淆矩阵对比\n",
    "n_models = min(len(model_results), 6)\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "axes = axes.ravel()\n",
    "\n",
    "class_names = [f'Person_{i}' for i in sorted(y.unique())]\n",
    "\n",
    "for i, (name, result) in enumerate(model_results.items()):\n",
    "    if i < 6:\n",
    "        cm = confusion_matrix(y_test, result['y_pred'])\n",
    "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[i], \n",
    "                    xticklabels=class_names, yticklabels=class_names)\n",
    "        axes[i].set_title(f'{name} 混淆矩阵', fontsize=12)\n",
    "        axes[i].set_xlabel('预测标签')\n",
    "        axes[i].set_ylabel('真实标签')\n",
    "\n",
    "# 隐藏多余的子图\n",
    "for i in range(n_models, 6):\n",
    "    axes[i].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 6.2 特征重要性分析\n",
    "feature_names = X_features.columns.tolist()\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Random Forest特征重要性\n",
    "if 'Random Forest' in model_results:\n",
    "    plt.subplot(2, 2, 1)\n",
    "    rf_model = model_results['Random Forest']['model']\n",
    "    rf_importance = rf_model.feature_importances_\n",
    "    rf_df = pd.DataFrame({'feature': feature_names, 'importance': rf_importance})\n",
    "    rf_df = rf_df.sort_values('importance', ascending=False).head(15)\n",
    "    \n",
    "    sns.barplot(data=rf_df, x='importance', y='feature')\n",
    "    plt.title('Random Forest 特征重要性 (Top 15)', fontsize=12)\n",
    "    plt.xlabel('重要性分数')\n",
    "\n",
    "# XGBoost特征重要性\n",
    "if 'XGBoost' in model_results:\n",
    "    plt.subplot(2, 2, 2)\n",
    "    xgb_model = model_results['XGBoost']['model']\n",
    "    xgb_importance = xgb_model.feature_importances_\n",
    "    xgb_df = pd.DataFrame({'feature': feature_names, 'importance': xgb_importance})\n",
    "    xgb_df = xgb_df.sort_values('importance', ascending=False).head(15)\n",
    "    \n",
    "    sns.barplot(data=xgb_df, x='importance', y='feature')\n",
    "    plt.title('XGBoost 特征重要性 (Top 15)', fontsize=12)\n",
    "    plt.xlabel('重要性分数')\n",
    "\n",
    "# LightGBM特征重要性\n",
    "if 'LightGBM' in model_results:\n",
    "    plt.subplot(2, 2, 3)\n",
    "    lgb_model = model_results['LightGBM']['model']\n",
    "    lgb_importance = lgb_model.feature_importances_\n",
    "    lgb_df = pd.DataFrame({'feature': feature_names, 'importance': lgb_importance})\n",
    "    lgb_df = lgb_df.sort_values('importance', ascending=False).head(15)\n",
    "    \n",
    "    sns.barplot(data=lgb_df, x='importance', y='feature')\n",
    "    plt.title('LightGBM 特征重要性 (Top 15)', fontsize=12)\n",
    "    plt.xlabel('重要性分数')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc344bb8-39a3-4ae1-82bd-2a31502df57d",
   "metadata": {},
   "source": [
    "## 人脸识别应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6e0e177-53e4-485c-ae49-516e08c28c67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤7：人脸识别应用函数 ===\n",
      "\n",
      "=== 测试人脸识别函数 ===\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本 1: 真实=2, 预测=2, 置信度=0.984\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本 2: 真实=36, 预测=36, 置信度=0.998\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本 3: 真实=19, 预测=19, 置信度=0.985\n"
     ]
    }
   ],
   "source": [
    "# 7. 人脸识别应用函数\n",
    "print(\"\\n=== 步骤7：人脸识别应用函数 ===\")\n",
    "\n",
    "def recognize_face(image_path, model, scaler=None):\n",
    "    \"\"\"\n",
    "    人脸识别函数\n",
    "    \n",
    "    参数:\n",
    "    image_path: 图片路径\n",
    "    model: 训练好的模型\n",
    "    scaler: 标准化器 (如果需要)\n",
    "    \n",
    "    返回:\n",
    "    识别结果字典\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 读取和预处理图片\n",
    "        img = Image.open(image_path)\n",
    "        img = img.convert('L')\n",
    "        img = img.resize((32, 32))\n",
    "        arr = np.array(img).flatten()\n",
    "        \n",
    "        # 创建特征\n",
    "        features = arr.tolist()\n",
    "        \n",
    "        # 添加统计特征\n",
    "        features.extend([\n",
    "            np.mean(arr),  # pixel_mean\n",
    "            np.std(arr),   # pixel_std\n",
    "            np.max(arr),   # pixel_max\n",
    "            np.min(arr),   # pixel_min\n",
    "            np.max(arr) - np.min(arr)  # pixel_range\n",
    "        ])\n",
    "        \n",
    "        # 计算对称性\n",
    "        img_reshaped = arr.reshape(32, 32)\n",
    "        left_half = img_reshaped[:, :16]\n",
    "        right_half = np.fliplr(img_reshaped[:, 16:])\n",
    "        symmetry = np.corrcoef(left_half.flatten(), right_half.flatten())[0, 1]\n",
    "        symmetry = symmetry if not np.isnan(symmetry) else 0\n",
    "        features.append(symmetry)\n",
    "        \n",
    "        # 转换为DataFrame\n",
    "        face_df = pd.DataFrame([features], columns=feature_names)\n",
    "        \n",
    "        # 标准化（如果需要）\n",
    "        if scaler is not None:\n",
    "            face_df_scaled = scaler.transform(face_df)\n",
    "            prediction = model.predict(face_df_scaled)\n",
    "            prediction_proba = model.predict_proba(face_df_scaled)\n",
    "        else:\n",
    "            prediction = model.predict(face_df)\n",
    "            prediction_proba = model.predict_proba(face_df)\n",
    "        \n",
    "        predicted_person = prediction[0]\n",
    "        confidence = prediction_proba[0].max()\n",
    "        \n",
    "        # 获取Top 3预测结果\n",
    "        top_3_indices = np.argsort(prediction_proba[0])[-3:][::-1]\n",
    "        top_3_predictions = []\n",
    "        for idx in top_3_indices:\n",
    "            person_name = f\"Person_{idx}\"\n",
    "            prob = prediction_proba[0][idx]\n",
    "            top_3_predictions.append({'person': person_name, 'probability': prob})\n",
    "        \n",
    "        return {\n",
    "            'predicted_person': predicted_person,\n",
    "            'confidence': confidence,\n",
    "            'top_3_predictions': top_3_predictions,\n",
    "            'success': True\n",
    "        }\n",
    "        \n",
    "    except Exception as e:\n",
    "        return {\n",
    "            'error': str(e),\n",
    "            'success': False\n",
    "        }\n",
    "\n",
    "# 测试识别函数\n",
    "print(\"\\n=== 测试人脸识别函数 ===\")\n",
    "# 使用测试集的前几个样本进行测试\n",
    "test_samples = X_test.head(3)\n",
    "test_labels = y_test.head(3)\n",
    "\n",
    "for i in range(len(test_samples)):\n",
    "    # 重构图片用于显示\n",
    "    original_features = X.iloc[y_test.index[i]].values.reshape(32, 32)\n",
    "    \n",
    "    plt.figure(figsize=(8, 3))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.imshow(original_features, cmap='gray')\n",
    "    plt.title(f'真实: Person {test_labels.iloc[i]}')\n",
    "    plt.axis('off')\n",
    "    \n",
    "    # 使用最佳模型进行识别\n",
    "    best_model_name = max(model_results.keys(), key=lambda x: model_results[x]['accuracy'])\n",
    "    best_model = model_results[best_model_name]['model']\n",
    "    \n",
    "    # 模拟识别过程\n",
    "    test_features = test_samples.iloc[i].values.reshape(1, -1)\n",
    "    \n",
    "    if best_model_name in ['Random Forest', 'XGBoost', 'LightGBM']:\n",
    "        prediction = best_model.predict(test_features)[0]\n",
    "        prediction_proba = best_model.predict_proba(test_features)[0]\n",
    "    else:\n",
    "        test_features_scaled = scaler_final.transform(test_features)\n",
    "        prediction = best_model.predict(test_features_scaled)[0]\n",
    "        prediction_proba = best_model.predict_proba(test_features_scaled)[0]\n",
    "    \n",
    "    confidence = prediction_proba.max()\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.bar(range(len(prediction_proba)), prediction_proba)\n",
    "    plt.title(f'预测: Person {prediction} (置信度: {confidence:.3f})')\n",
    "    plt.xlabel('Person ID')\n",
    "    plt.ylabel('Probability')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(f\"样本 {i+1}: 真实={test_labels.iloc[i]}, 预测={prediction}, 置信度={confidence:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9c4862-a82d-452b-a6e0-cccb01c8e3a9",
   "metadata": {},
   "source": [
    "# 模型总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "045fc623-f547-4959-aced-1e88c2632f93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 步骤8：模型总结和保存 ===\n",
      "\n",
      "============================================================\n",
      "人脸识别模型 - 最终总结报告\n",
      "============================================================\n",
      "\n",
      "数据集信息:\n",
      "  总样本数: 400\n",
      "  特征数量: 1030\n",
      "  人员数量: 40\n",
      "  图片尺寸: 32x32 像素\n",
      "\n",
      "各模型性能对比 (按准确率排序):\n",
      "  1. LightGBM:\n",
      "     准确率: 0.9125\n",
      "     F1分数: 0.8992\n",
      "\n",
      "最佳模型: LightGBM\n",
      "  准确率: 0.9125\n",
      "  精确率: 0.9125\n",
      "  召回率: 0.9125\n",
      "  F1分数: 0.8992\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n=== 步骤8：模型总结和保存 ===\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"人脸识别模型 - 最终总结报告\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "print(f\"\\n数据集信息:\")\n",
    "print(f\"  总样本数: {len(X)}\")\n",
    "print(f\"  特征数量: {X_features.shape[1]}\")\n",
    "print(f\"  人员数量: {len(y.unique())}\")\n",
    "print(f\"  图片尺寸: 32x32 像素\")\n",
    "\n",
    "print(f\"\\n各模型性能对比 (按准确率排序):\")\n",
    "sorted_models = sorted(model_results.items(), key=lambda x: x[1]['accuracy'], reverse=True)\n",
    "for i, (name, result) in enumerate(sorted_models, 1):\n",
    "    print(f\"  {i}. {name}:\")\n",
    "    print(f\"     准确率: {result['accuracy']:.4f}\")\n",
    "    print(f\"     F1分数: {result['f1']:.4f}\")\n",
    "\n",
    "# 选择最佳模型\n",
    "best_model_name = sorted_models[0][0]\n",
    "best_model = model_results[best_model_name]['model']\n",
    "\n",
    "print(f\"\\n最佳模型: {best_model_name}\")\n",
    "print(f\"  准确率: {model_results[best_model_name]['accuracy']:.4f}\")\n",
    "print(f\"  精确率: {model_results[best_model_name]['precision']:.4f}\")\n",
    "print(f\"  召回率: {model_results[best_model_name]['recall']:.4f}\")\n",
    "print(f\"  F1分数: {model_results[best_model_name]['f1']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7de1f2d-e030-4cf2-a10d-8a12409a1816",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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